diff --git a/.gitattributes b/.gitattributes
index 9f82ae6e9bfe2db51c792a07554f4c788c3a2379..c6ec93209d7668bbc09d1ba44003f4d897fc4bbc 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -23003,3 +23003,21 @@ ReInterHand/shared-v1.0/data/m--20221110--1033--TQH976--pilot--ProjectGoliathScr
 OakInk-v2/shared-v1.0/data/scene_02__A004++seq__b4b28ce5e8b2ee9ca061__2023-05-07-18-38-10/104422070904/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
 ReInterHand/shared-v1.0/data/m--20221110--1033--TQH976--pilot--ProjectGoliathScript--Hands--two-hands__envmap_per_segment__120092__123277/401129/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
 ReInterHand/shared-v1.0/data/m--20221110--1033--TQH976--pilot--ProjectGoliathScript--Hands--two-hands__envmap_per_segment__120092__123277/401394/annotation/segment/mask.mkv filter=lfs diff=lfs merge=lfs -text
+EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/annotation/segment/mask.mkv filter=lfs diff=lfs merge=lfs -text
+EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
+EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/annotation/segment/mask.mkv filter=lfs diff=lfs merge=lfs -text
+EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
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+EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
+EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask.mkv filter=lfs diff=lfs merge=lfs -text
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+EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
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+EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb.mkv filter=lfs diff=lfs merge=lfs -text
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/annotation/segment/instances.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4bc582d80195230937cdc8b627a496736334b61e
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/annotation/segment/instances.jsonl
@@ -0,0 +1,101 @@
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diff --git a/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..7d9fdc958eb6d507ac18bd78e52ca66ad7e95760
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/encoding_validation.json
@@ -0,0 +1,370 @@
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+        ],
+        "decode_verify_seconds": 1.287460213000486,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "1920x1080",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_1920x1080_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..69a068af77f68e6ab5e2da1768df407e4bd73b7c
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_1920x1080_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 101,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      1920,
+      1080
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          1920,
+          1080
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          1920,
+          1080
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_1920x1080_000000/ego/observation/rgb.mkv
new file mode 100644
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diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..b3fa16413f95806eb0d72015e442c5074a158cf2
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/encoding_validation.json
@@ -0,0 +1,451 @@
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+        ],
+        "decode_verify_seconds": 2.1526317210000343,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "1920x1440",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_1920x1440_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..5b9dae41a8726ce1d4f18486df097bf505a0bbfd
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_1920x1440_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 128,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      1920,
+      1440
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          1920,
+          1440
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          1920,
+          1440
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
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+size 31398146
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_1920x1440_000000/ego/observation/rgb_valid.pt
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diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/annotation/segment/instances.jsonl
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diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..bcd027e973eaa23768d5e220f7576daa2f57965c
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/encoding_validation.json
@@ -0,0 +1,415 @@
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+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  },
+  "segment": {
+    "selected": "ffv1",
+    "candidates": [
+      {
+        "encoder": "ffv1",
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+        ],
+        "decode_verify_seconds": 1.956923708999966,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "1920x1440",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_1920x1440_000001/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..2704885e5ce4139e1658b972b00f0cce14786549
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_1920x1440_000001",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 116,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      1920,
+      1440
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          1920,
+          1440
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
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+            "libavdevice": [
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+              3,
+              102
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+            "libavfilter": [
+              11,
+              14,
+              102
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+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          1920,
+          1440
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..95f747381503463db752b3bc31d3010287b5c24c
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9fe0d05779414fe0122dd7169df0fc2cdf94e63a527bae4d594e8754c221684
+size 36402571
diff --git a/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb_valid.pt
new file mode 100644
index 0000000000000000000000000000000000000000..6aa1ebb57aa7e86fd184d9e75ca445c7b08a8f13
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_1920x1440_000001/ego/observation/rgb_valid.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84aa3555e0521f7bdb5a14f14c5750e6e5abd609f78820fbe091a7ae617a693c
+size 1655
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/instances.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..65c557329add5ccc45fcc97e8691b49387709b19
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/instances.jsonl
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diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask.mkv b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask.mkv
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--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask.mkv
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+size 120120
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask_valid.pt b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask_valid.pt
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+++ b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask_valid.pt
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diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/camera/frames.jsonl b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/camera/frames.jsonl
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index 0000000000000000000000000000000000000000..1f2a5ad64870d70c1f43f6e77090456b1f47c894
--- /dev/null
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diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..492368313eb2aca2b40686baeb2edf488fa6a9ea
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/encoding_validation.json
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+{
+  "rgb": {
+    "selected": "copy",
+    "candidates": [
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+        "bytes": 759698,
+        "encode_seconds": 0.0014063999997233623,
+        "frames": 3,
+        "all_decoded_frames_exact": true,
+        "ordered_decoded_sha256": [
+          "38a772deba51ffac8fdfadeb9bd4733a9ba5518b92adea22caa4307cf3fb87f9",
+          "9134d170cd3e160fa604a9b42fa3c87a794ba23ed5b14b4899085644dc32523c",
+          "ae2b559c642a834b25b12e1fc1a41f8c2fc68344b1aab022700c7286e863d11f"
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+        "packet_count": 3,
+        "ordered_packet_sha256": [
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+          "a6d4af56d7e67e1db977ec0c5f44ce6c42024ed8dcd5f6414bc3af85abd96bd6",
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+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
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+    "selected": "ffv1",
+    "candidates": [
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+        "encoder": "ffv1",
+        "bytes": 120120,
+        "encode_seconds": 0.31031789500048035,
+        "frames": 3,
+        "all_decoded_frames_exact": true,
+        "ordered_decoded_sha256": [
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+        "command": [
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+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "2560x1440",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_2560x1440_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..3a5083fbf851c789d3896d130cddf4236fc3bc08
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_2560x1440_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 3,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      2560,
+      1440
+    ]
+  },
+  "components": {
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+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
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+        "field": "source_locator",
+        "records": "all frame_index rows"
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+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
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+          1440
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+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
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+          "version": "18.1.0",
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+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
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+          1440
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
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+              102
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+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_2560x1440_000000/ego/observation/rgb.mkv
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diff --git a/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e021b2221ab49bdb2e176867827e2c4929965c2
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/encoding_validation.json
@@ -0,0 +1,334 @@
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+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  },
+  "segment": {
+    "selected": "ffv1",
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+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "2560x1920",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_2560x1920_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..38da01f60ca6526b5d263c9a2da23f759f8e05b9
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_2560x1920_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 89,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      2560,
+      1920
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          2560,
+          1920
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+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
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+              26,
+              102
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+            "libavcodec": [
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+              28,
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+              12,
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+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          2560,
+          1920
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
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+            "libavcodec": [
+              62,
+              28,
+              102
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+            "libavformat": [
+              62,
+              12,
+              102
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+            "libavdevice": [
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+              3,
+              102
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+            "libavfilter": [
+              11,
+              14,
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+            "libswscale": [
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+              5,
+              102
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+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb.mkv
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--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e7203d66e28a3b00273aef1c57b627183583617211fc1e5cbb93e1dbf372ed2
+size 39574102
diff --git a/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb_valid.pt
new file mode 100644
index 0000000000000000000000000000000000000000..3c00387c51e8f97394f53c8013dcf964fc8d1a50
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_2560x1920_000000/ego/observation/rgb_valid.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 1655
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/instances.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd7d74a6f6c24c2e8e37c0dd485dda48056ce20f
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/instances.jsonl
@@ -0,0 +1 @@
+{"frame_index": 0, "labels": [{"mask_value": 9, "category_id": "background", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 0}}, {"mask_value": 1, "category_id": "left_hand", "entity_id": "hand_left", "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 1}}, {"mask_value": 2, "category_id": "right_hand", "entity_id": "hand_right", "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 2}}, {"mask_value": 3, "category_id": "first_order_left", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 3}}, {"mask_value": 4, "category_id": "first_order_right", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 4}}, {"mask_value": 5, "category_id": "first_order_both", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 5}}, {"mask_value": 6, "category_id": "second_order_left", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 6}}, {"mask_value": 7, "category_id": "second_order_right", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 7}}, {"mask_value": 8, "category_id": "second_order_both", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 8}}]}
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/mask.mkv b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/mask.mkv
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diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/mask_valid.pt b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/mask_valid.pt
new file mode 100644
index 0000000000000000000000000000000000000000..5f9bfcfdf996a00c34de59d6cbb9465b841f45a5
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/mask_valid.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 1598
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/camera/frames.jsonl b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/camera/frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f79096e24148997527d43aa17e07e0d123724da
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/camera/frames.jsonl
@@ -0,0 +1 @@
+{"frame_index": 0, "sample_id": "val/epic_P12_05_frame_0000001171.jpg", "source_frame_id": "epic_P12_05_frame_0000001171.jpg", "timestamp_ns": null, "clock_domain": null, "source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/image/epic_P12_05_frame_0000001171.jpg", "extracted_path": "data/val/image/epic_P12_05_frame_0000001171.jpg", "extracted_sha256": "33eaf4c0059f947d26897d7fb531cc71705a08f46b666e344aa1a67f45ebb8fe"}, "split": "val", "source_split": "val", "mask_source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/label/epic_P12_05_frame_0000001171.png", "extracted_path": "data/val/label/epic_P12_05_frame_0000001171.png", "extracted_sha256": "836f89574ad492e087c3a37ea368b33637769a4ea281717898b2a632e50678f3"}}
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..502773e2402b24e80939b1a2ba0373623300e884
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/encoding_validation.json
@@ -0,0 +1,70 @@
+{
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+    "candidates": [
+      {
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+        "bytes": 11272,
+        "encode_seconds": 0.00043690900020010304,
+        "frames": 1,
+        "all_decoded_frames_exact": true,
+        "ordered_decoded_sha256": [
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+        ],
+        "decode_verify_seconds": 0.003062848999434209,
+        "all_jpeg_packets_exact": true,
+        "packet_count": 1,
+        "ordered_packet_sha256": [
+          "33eaf4c0059f947d26897d7fb531cc71705a08f46b666e344aa1a67f45ebb8fe"
+        ]
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+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  },
+  "segment": {
+    "selected": "ffv1",
+    "candidates": [
+      {
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+        "bytes": 2139,
+        "encode_seconds": 0.04283333200055495,
+        "frames": 1,
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+        "ordered_decoded_sha256": [
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+        "decode_verify_seconds": 0.0020970320001652,
+        "command": [
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+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "342x256",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_342x256_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..7e2739da7d193f3507ff8923c10ec929292a9527
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_342x256_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 1,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "single",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      342,
+      256
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          342,
+          256
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          342,
+          256
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/observation/rgb.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..4042b6580638743f251bc1ea816b8e3ec53e364b
Binary files /dev/null and b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/observation/rgb.mkv differ
diff --git a/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/observation/rgb_valid.pt
new file mode 100644
index 0000000000000000000000000000000000000000..01630ca881416edda9f05e148f7ff54e88cc3919
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_342x256_000000/ego/observation/rgb_valid.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d9e569dc59411256267f0f3ab6ce1f8cce4670e9c2d27e90f156cbc5b214dc8
+size 1591
diff --git a/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/annotation/segment/instances.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..826121beb7917626aeec21c4be662f25fcc4636d
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/annotation/segment/instances.jsonl
@@ -0,0 +1,20 @@
+{"frame_index": 0, "labels": [{"mask_value": 9, "category_id": "background", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 0}}, {"mask_value": 1, "category_id": "left_hand", "entity_id": "hand_left", "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 1}}, {"mask_value": 2, "category_id": "right_hand", "entity_id": "hand_right", "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 2}}, {"mask_value": 3, "category_id": "first_order_left", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 3}}, {"mask_value": 4, "category_id": "first_order_right", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 4}}, {"mask_value": 5, "category_id": "first_order_both", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 5}}, {"mask_value": 6, "category_id": "second_order_left", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 6}}, {"mask_value": 7, "category_id": "second_order_right", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 7}}, {"mask_value": 8, "category_id": "second_order_both", "entity_id": null, "part_id": null, "source_instance_id": null, "source_attributes": {"source_class_id": 8}}]}
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+{"frame_index": 16, "sample_id": "val/escape_GX010070_002280.jpg", "source_frame_id": "escape_GX010070_002280.jpg", "timestamp_ns": null, "clock_domain": null, "source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/image/escape_GX010070_002280.jpg", "extracted_path": "data/val/image/escape_GX010070_002280.jpg", "extracted_sha256": "a05d74687ef459b5ef881794723e417fba94f37e07e189f90b200fc622002c69"}, "split": "val", "source_split": "val", "mask_source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/label/escape_GX010070_002280.png", "extracted_path": "data/val/label/escape_GX010070_002280.png", "extracted_sha256": "a1bd937d97922f391993fdc5a6c0852f707ea98ad51ea5cf0fa03ad1a047a334"}}
+{"frame_index": 17, "sample_id": "val/escape_GX010071_001560.jpg", "source_frame_id": "escape_GX010071_001560.jpg", "timestamp_ns": null, "clock_domain": null, "source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/image/escape_GX010071_001560.jpg", "extracted_path": "data/val/image/escape_GX010071_001560.jpg", "extracted_sha256": "b4907d2e649b9ee1ec2106899eb35d12820ecd74281b819a4b9e94487305de24"}, "split": "val", "source_split": "val", "mask_source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/label/escape_GX010071_001560.png", "extracted_path": "data/val/label/escape_GX010071_001560.png", "extracted_sha256": "03dc9d59301484043fc65ec08e0b9ab9868062f5965892701695a07b06a657d9"}}
+{"frame_index": 18, "sample_id": "val/escape_GX010071_009720.jpg", "source_frame_id": "escape_GX010071_009720.jpg", "timestamp_ns": null, "clock_domain": null, "source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/image/escape_GX010071_009720.jpg", "extracted_path": "data/val/image/escape_GX010071_009720.jpg", "extracted_sha256": "ce4d006c4c12cc41e945f9d4842206d1f558f8b3acc791b4e19b01f64c59d44e"}, "split": "val", "source_split": "val", "mask_source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/label/escape_GX010071_009720.png", "extracted_path": "data/val/label/escape_GX010071_009720.png", "extracted_sha256": "e99d6cf9798061d4138db8b4c3cc6a1093764a2ab4dd801b832009aedc7eaeb7"}}
+{"frame_index": 19, "sample_id": "val/escape_GX010071_009900.jpg", "source_frame_id": "escape_GX010071_009900.jpg", "timestamp_ns": null, "clock_domain": null, "source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/image/escape_GX010071_009900.jpg", "extracted_path": "data/val/image/escape_GX010071_009900.jpg", "extracted_sha256": "5d40230fe9b79c1d9da06c26d65b19db6c4ec0f86c4e7c4aace9bb74dc3b7cc1"}, "split": "val", "source_split": "val", "mask_source_locator": {"repo": "ParityLab/Hand-Datasets-Raw", "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798", "file": "EgoHOS/raw/data/egohos_dataset.zip", "archive_sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5", "member": "data/val/label/escape_GX010071_009900.png", "extracted_path": "data/val/label/escape_GX010071_009900.png", "extracted_sha256": "7df68edc3eee142afc2b51531df3949f0e27c9a44b8c333f3c2aa3050bce4759"}}
diff --git a/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..af2322487103be14d7e6fa572a8efe25ca31d2e2
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/encoding_validation.json
@@ -0,0 +1,127 @@
+{
+  "rgb": {
+    "selected": "copy",
+    "candidates": [
+      {
+        "encoder": "copy",
+        "bytes": 8023403,
+        "encode_seconds": 0.00962819300002593,
+        "frames": 20,
+        "all_decoded_frames_exact": true,
+        "ordered_decoded_sha256": [
+          "f732a4fb92a12934f9cebdd7e1d59fb576f8eb68330efe5e34ae2e51e4d4c4e4",
+          "4532033e8f2ebfc2ba4fa30b136c9b9795f31402805f3bef3b4bb2653cbc42c6",
+          "8ae63525edde42c788314cccc086971edcca428589b64c355ca6894cab78017a",
+          "ca9394694de469dcd200c73c31316fc31871a6ac1d92f51090db8cf317ac665f",
+          "91c37ebac4dea58ee08e6486b0f658a08b50cb2d867e025c60f380920b43246e",
+          "9cbd3c4b375ac7546da34208590dc1004ed07a0d462acc1d1f756b692553c815",
+          "b67180671f263d1bb637b223bc05ec1cb240ed28abf8d5d78694f85117131694",
+          "fcfb531a60baae370580c0b6bf96aec82f6a7ce110905f29ea69f4ce2d76389f",
+          "ddedd62141e579118cc81698810fe1ccd684468edbb0a0514c2677d48e3510af",
+          "fe45f1e3c3016b61c031fa659834456653f62453de7ad10552ddd5b60aaa8526",
+          "ebc074a8e2579af4816d5e6a172cd9251d67b95d1667f83d2c0061ea87b09975",
+          "049e1d06d856167c45b8012e4ca2d3223b834ac9e6c1b66dd65ff639a09c1d9c",
+          "f415a9d6e0fe1dca4aa9c73822fafe8ce88d7bcb96c49081c10ee374b08e45f5",
+          "414368a693975e02feb02c2698246266fbfc259ebe6d397f81c652fc35cd42bb",
+          "b13d772926b4b9bc62c333b9da2d63f5204ef91b079336955a56618d52507791",
+          "2262ce4c2243bc2714e68fad18b1b20d8e2ede13a776e90eae51ee7fe852de8f",
+          "5d47247bdaf2621c556a3b2e6f99f95d09776bd19e0d7e95b60519a552b43117",
+          "69099ed352d698146a16bdcd731e1102b92ae12b19112d6df49cc8e61d1d2d68",
+          "5fc4a8e755d73716bc1744f1f6f3e22fcb1ec5327d51594a332e4e7ad43642d4",
+          "6d7524f3a977423134c9a2c3a4ea8974b42d009aa88fa59278a4089493aef17f"
+        ],
+        "decode_verify_seconds": 0.6881287389996942,
+        "all_jpeg_packets_exact": true,
+        "packet_count": 20,
+        "ordered_packet_sha256": [
+          "f9e888374cdedb0900d9111ea37fb2d49c7114c228cc3b0562eb7ff5da72540c",
+          "6c9085745aab1b31200cd343820ce86dc56d0fee9c540b496a34b07654a1471c",
+          "c794bf03f740a6f892651312b29d2f3218664911e40c2afe84523422ba7ad0c9",
+          "b338bc0b3075f00785dfefd81ea9edb13d9dabf71e1079b88c6e1f88a58edef3",
+          "d89c8c84e812d1e3b3a5a28f0970ad0bedcca2b50a4aafd075e2b77a2745fa07",
+          "a9691acd28ea16d7b8b257d85c43f0c2691ac09291bee453409acf4e43e202ef",
+          "3ef65871138c50393d4cf7eadc1aff0c1a1a3a28f446cd779958dd0c490f14fb",
+          "603e95afb8819a7711d831088a162710c80d290d60b6818fceaadafad081a54b",
+          "6092deda9fb08537b6aa6fac4ce254ece22a06d956aa818a6d4d0beba1747e47",
+          "f37451f309d1d8be38de9f4805c8fabb1d24c22221f4d7e1d8d6c93b17076a0f",
+          "416293c8c7b7d3255fe9a98de4d4086b78d43855aff9ffb8c6c78d51484f9203",
+          "3b6d9004a9c793da02866c1ba324b2c8c4188b4bd3388ac29070842cdd835e09",
+          "b8f0f08bd6a0d1fb24cda6a71093b1a32e2e805a9d4cda069d39193a1885df93",
+          "a93ec8163f8cf5ff517bf49fa37dde57e3364c9c6e63ec9a5e066c7452651591",
+          "686b261c9641b03046bbc4b68693722a870256c10ae9b28fca6825b3ac816f7d",
+          "1dcc7a778f56d187ad87c3e50681c1dfa503dfe619b3a8d722525625b3551655",
+          "a05d74687ef459b5ef881794723e417fba94f37e07e189f90b200fc622002c69",
+          "b4907d2e649b9ee1ec2106899eb35d12820ecd74281b819a4b9e94487305de24",
+          "ce4d006c4c12cc41e945f9d4842206d1f558f8b3acc791b4e19b01f64c59d44e",
+          "5d40230fe9b79c1d9da06c26d65b19db6c4ec0f86c4e7c4aace9bb74dc3b7cc1"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  },
+  "segment": {
+    "selected": "ffv1",
+    "candidates": [
+      {
+        "encoder": "ffv1",
+        "bytes": 1737458,
+        "encode_seconds": 1.513338365999516,
+        "frames": 20,
+        "all_decoded_frames_exact": true,
+        "ordered_decoded_sha256": [
+          "848a16851851da8dc18c09eedb611dda278fe99e52210ee6a711b14d024ae350",
+          "eab3de01ed0450be5d8f6bab7b16879d91c8450c5ed311d63883f2096f480552",
+          "1fd829a8f5974defb3e94dd809ffb503326fcbc37dcbf02b6919fcc5a034b036",
+          "6cbb89f11474ec6a9d1b4435350f9214e090ad2c9a8d2ab4e79741a96a15d49d",
+          "139a67093b5bc01255c16deab3562ccabd563d5c1f09571782cbedb4b2f68c15",
+          "f87fa36253876a6d1fdfa0c219d57ab0274ebb7e2d9ebdf35a8d859764f7a29f",
+          "962fcb7d0b7353258384322ea184ab1db8b62dff4b183e6305013a8f54c252ca",
+          "b70cbaf5bafd06b0e93ae2851e532f21b30ff5011defa032081b8327148f9dd6",
+          "6f05ea75c52f82c60c3d553b575f2cd26dde1c9438f01917ca0f1c7fdcf221ce",
+          "6bdcce9715e5c509df3bbc01aae057b8bfe9621e6fbf79d592a45692e1c3f718",
+          "18a633185978c877d1fd019369cc0e9e96c30342b1428eea38af499e408ee31a",
+          "be6ff32c670596cfc27858394e4012ff7e7ca7bfaaabd5c8b7a58cc568eeed63",
+          "82f60a6bef92ca492d4b3d6940f42f7bb96b1cf306bcf64f0b3e35bc19022685",
+          "be429c43ad56d9e33e9602155df2bbfb3a958f055f37ba547de826609f9488e4",
+          "881991c12b263982a8543888849e205b82b47d5429a624eaecdb229c2d40f269",
+          "9338275501e44e1971a170a4021c8ef2b45ae9f70e908d9e5accf25747f8ef44",
+          "ec054f962c66c8b2e3376267ebc97b22ee113135d0a89e0d30aa2364800f4a90",
+          "978febdf293008659a519334715ca6d985280d1ba8c562ebd5ec13660c4cbde7",
+          "fbd4dcbf2e69be8c13c4f43a8dd03a1b6b237e7e7bb188bb75b708790b49aec8",
+          "07d0be3a632830db59b34709d9b8b0d16d48ab6b64216b90e40d9e5f175b5d0a"
+        ],
+        "decode_verify_seconds": 1.2330826010002056,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "3840x2160",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_3840x2160_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bda10388a9657c80e1f772bc1e0c19131854149
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_3840x2160_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 20,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      3840,
+      2160
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          3840,
+          2160
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
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+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
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+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
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diff --git a/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_3840x2160_000000/ego/observation/rgb_valid.pt
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diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/annotation/segment/instances.jsonl
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diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..89d342a051c3c5b1e02a950a0e46816d323eabc7
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/encoding_validation.json
@@ -0,0 +1,451 @@
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+    "random_access": "sequential decode from beginning"
+  },
+  "segment": {
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+          "1655fd6ffad42968cc1fdf23b408d241e818c0747a4f756492cd081d2e9d7035"
+        ],
+        "decode_verify_seconds": 0.1149307730001965,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "456x256",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_456x256_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..165d08ee721069eb60df4617fee9603b81ab42d6
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_456x256_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 128,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      456,
+      256
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
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+          256
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+        "storage_fps": 1,
+        "timebase_role": "index_only",
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+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
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+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
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+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          456,
+          256
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
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+            "libavcodec": [
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+              28,
+              102
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+            "libswresample": [
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+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
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diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_456x256_000000/ego/observation/rgb_valid.pt
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diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/annotation/segment/instances.jsonl
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diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..017c5c9f11fab99f8039bdcb43ad2f69edb691f0
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/encoding_validation.json
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+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  },
+  "segment": {
+    "selected": "ffv1",
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+      {
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+        ],
+        "decode_verify_seconds": 0.0643323539998164,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "456x256",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_456x256_000001/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..c5b350944494c867f6ebc5a63ef4cd3a459434f4
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_456x256_000001",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 89,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      456,
+      256
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          456,
+          256
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
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+              28,
+              102
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+            "libavformat": [
+              62,
+              12,
+              102
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+            "libavdevice": [
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+            "libavfilter": [
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+              5,
+              102
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+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          456,
+          256
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
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+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
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+              12,
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+            "libavdevice": [
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+              3,
+              102
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+            "libavfilter": [
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+              14,
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+            "libswscale": [
+              9,
+              5,
+              102
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+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/observation/rgb.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..6828cab5bcd1211ebd376379a06a3f5cec33820f
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/observation/rgb.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fe1118b53906f21d20ff6f4e66879e866d40728de04fb7a96a635d4b8783843
+size 1639873
diff --git a/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/observation/rgb_valid.pt
new file mode 100644
index 0000000000000000000000000000000000000000..3c00387c51e8f97394f53c8013dcf964fc8d1a50
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_456x256_000001/ego/observation/rgb_valid.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c58bcebf58e109f4139a1a1c13957a19ee3a69fc3de24b2c786ebc6644b733e
+size 1655
diff --git a/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/annotation/segment/instances.jsonl b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/annotation/segment/instances.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..36af32b09cd432c4ef01795a78cdbc8e341eaf81
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/annotation/segment/instances.jsonl
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diff --git a/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/encoding_validation.json b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/encoding_validation.json
new file mode 100644
index 0000000000000000000000000000000000000000..a3686b635421ce1787428de64baa247c1b80c265
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/encoding_validation.json
@@ -0,0 +1,301 @@
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+        ],
+        "decode_verify_seconds": 0.14173250200019538,
+        "command": [
+          "ffmpeg",
+          "-hide_banner",
+          "-loglevel",
+          "error",
+          "-y",
+          "-f",
+          "rawvideo",
+          "-pixel_format",
+          "gray16le",
+          "-video_size",
+          "640x480",
+          "-framerate",
+          "1",
+          "-i",
+          "pipe:0",
+          "-an",
+          "-c:v",
+          "ffv1",
+          "-level",
+          "3",
+          "-pix_fmt",
+          "gray16le",
+          "outputs/EgoHOS-full/shared-v1.0/data/val_640x480_000000/ego/annotation/segment/mask.ffv1.mkv"
+        ]
+      }
+    ],
+    "failed_candidates": [],
+    "pixel_coverage": "ALL frames, count and order",
+    "random_access": "sequential decode from beginning"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/metainfo.json b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/metainfo.json
new file mode 100644
index 0000000000000000000000000000000000000000..0a5d5c79f96fdda26174886f88630a5b3e15e89c
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/metainfo.json
@@ -0,0 +1,255 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "dataset_id": "EgoHOS",
+  "release_id": "shared-v1.0",
+  "sequence_id": "val_640x480_000000",
+  "camera_id": "ego",
+  "sample_type": "image",
+  "num_frames": 78,
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_type": "ego",
+  "split": "val",
+  "sampling": {
+    "type": "independent",
+    "source_fps": null,
+    "frames_ref": "camera/frames.jsonl",
+    "strategy": "full",
+    "seed": 20260928,
+    "timestamps": "unknown; no timestamps inferred from filenames or MKV PTS"
+  },
+  "camera": {
+    "model": "unknown",
+    "schema_id": null,
+    "image_size": [
+      640,
+      480
+    ]
+  },
+  "components": {
+    "rgb": {
+      "available": true,
+      "schema_id": "rgb/uint8/v1",
+      "data_ref": "observation/rgb.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width",
+        "RGB"
+      ],
+      "valid_ref": "observation/rgb_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "exact source JPEG remux or exact RGB round-trip re-encoding"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "source_locator",
+        "records": "all frame_index rows"
+      },
+      "storage": {
+        "profile": "mkv/mjpeg_copy/v1",
+        "codec": "mjpeg",
+        "pixel_format": "yuvj420p",
+        "encoded_size": [
+          640,
+          480
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": true,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "rgb24",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "copy",
+          "stream_copy": true,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      }
+    },
+    "segment": {
+      "available": true,
+      "schema_id": "segment/uint16_labels/v1",
+      "data_ref": "annotation/segment/mask.mkv",
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "valid_ref": "annotation/segment/mask_valid.pt",
+      "valid_axes": [
+        "T"
+      ],
+      "provenance": {
+        "kind": "format_conversion",
+        "method": "source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip"
+      },
+      "source_locator": {
+        "file": "camera/frames.jsonl",
+        "field": "mask_source_locator"
+      },
+      "storage": {
+        "profile": "mkv/uint16_ffv1/v1",
+        "codec": "ffv1",
+        "pixel_format": "gray16le",
+        "encoded_size": [
+          640,
+          480
+        ],
+        "storage_fps": 1,
+        "timebase_role": "index_only",
+        "source_lossy": false,
+        "decoder": {
+          "name": "PyAV",
+          "version": "18.1.0",
+          "libraries": {
+            "libavutil": [
+              60,
+              26,
+              102
+            ],
+            "libavcodec": [
+              62,
+              28,
+              102
+            ],
+            "libavformat": [
+              62,
+              12,
+              102
+            ],
+            "libavdevice": [
+              62,
+              3,
+              102
+            ],
+            "libavfilter": [
+              11,
+              14,
+              102
+            ],
+            "libswscale": [
+              9,
+              5,
+              102
+            ],
+            "libswresample": [
+              6,
+              3,
+              102
+            ]
+          },
+          "output": "gray16le",
+          "rotation": "none",
+          "color_interpretation": "libswscale source metadata/defaults; identical source/target decoder",
+          "source_color_space": "unknown"
+        },
+        "encoder_parameters": {
+          "name": "ffv1",
+          "stream_copy": false,
+          "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers",
+          "crf": null,
+          "gop": null
+        }
+      },
+      "segmentation_type": "semantic",
+      "mapping_ref": "annotation/segment/instances.jsonl",
+      "coverage": {
+        "kind": "partial",
+        "meaning": "hands and selected interacting object categories; background is source task background, not exhaustive object annotation"
+      },
+      "valid_policy": {
+        "rule": "paired source label exists, shape and integer categories verified; no additional official QA supplied",
+        "local_excluded_frame_indices": []
+      }
+    },
+    "depth": {
+      "available": false,
+      "reason": "not provided by selected source image release"
+    },
+    "mano": {
+      "available": false,
+      "reason": "not exported: model profile/flat-hand-mean/translation conversion not empirically verified"
+    },
+    "visibility": {
+      "available": false,
+      "reason": "no independent source visibility input; never inferred from valid"
+    },
+    "contact": {
+      "available": false,
+      "reason": "raw package lacks contact/; interaction-order categories are not per-entity contact labels"
+    },
+    "intrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "extrinsic": {
+      "available": false,
+      "reason": "source calibration absent"
+    },
+    "keypoints": {
+      "available": false,
+      "reason": "source keypoints absent"
+    },
+    "object": {
+      "available": false,
+      "reason": "semantic classes do not establish instance identity/mesh/pose"
+    }
+  },
+  "split_scheme": "EgoHOS-official-four-directories",
+  "split_source_locator": {
+    "file": "camera/frames.jsonl",
+    "field": "source_locator",
+    "records": "all frame_index rows"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb.mkv b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb.mkv
new file mode 100644
index 0000000000000000000000000000000000000000..c8331b3e9091ef8c9d9e337c99f3ffe2517337d6
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb.mkv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fa6ed170254275359c2244d02e69376af0df5308cbd6128f0918017db10fd05
+size 3343442
diff --git a/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb_valid.pt b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb_valid.pt
new file mode 100644
index 0000000000000000000000000000000000000000..768a15e7d9b5babdbc6b87e8eeee90eec286c826
--- /dev/null
+++ b/EgoHOS/shared-v1.0/data/val_640x480_000000/ego/observation/rgb_valid.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5672e16232ba0db95f8faec383df4ec862dcb0637189f55e8e63e70a92442d9c
+size 1655
diff --git a/EgoHOS/shared-v1.0/doc/CONTRACT.md b/EgoHOS/shared-v1.0/doc/CONTRACT.md
new file mode 100644
index 0000000000000000000000000000000000000000..e80d21a1c4af1bfaa2f429b3395a4304e6af6eb0
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/CONTRACT.md
@@ -0,0 +1,459 @@
+# 手物数据仓库统一约定 v1.0
+
+格式版本：`hand-warehouse/1.0`。存储约定：`lossless-video-2026-09-18`。
+
+范围限于手物交互重建。按本任务纳入的源模态转换，不要求每个数据集填满所有组件。缺失模态、尚未下载、语义未核实分别报告，不能互相替代。
+
+shared 层不是 raw 的完整镜像。处理者只需提取本次任务范围明确要求、语义已经核实且能够按本规范可靠转换的字段和模态。raw 中其余字段可以不纳入 shared，但必须在 `RAW_TO_SHARED.md` 中列出主要未纳入内容及原因。不得为了覆盖全部 raw 字段而猜测语义、虚构模态或保存未经定义的字段。未纳入 shared 不等于源数据不存在，原始信息仍以 raw 为准。
+
+本文定义各字段的含义与使用规则，机器可读注册表见 [schema.json](schema.json)。两者互相补充；如有冲突，应先核实并统一定义。
+
+## 1. 目录
+
+```text
+hand-datasets/
+  <dataset_name>/
+    raw/
+      doc/                         # 官方说明、来源与校验记录
+      script/                      # 官方下载脚本、必要包装脚本
+      data/                        # 保留原始包，不原地解压或改写
+    <dataset_version>/
+      doc/                         # 发布说明、schema、来源清单
+      script/                      # 转换、校验脚本及运行配置
+      data/
+        <sequence_id>/
+          <camera_id>/
+            metainfo.json
+            observation/
+              rgb.mkv
+              rgb_valid.pt
+              depth.mkv           # float32 深度的可逆视频编码
+              depth_valid.mkv     # 逐像素 bool 的无损视频编码
+            camera/
+              frames.jsonl
+              intrinsic.pt
+              intrinsic_valid.pt
+              extrinsic.pt
+              extrinsic_valid.pt
+            annotation/
+              mano/
+                motion.pt
+                motion_valid.pt
+                visibility.pt
+              keypoints/
+                uv.pt
+                uv_valid.pt
+                xyz.pt
+                xyz_valid.pt
+                visibility.pt    # 源数据有时提供
+              object/
+                <object_id>/
+                  assets/          # 该物体的网格、材质、纹理及部件资产
+                    <mesh_id>.obj
+                  pose.pt
+                  pose_valid.pt
+                  visibility.pt
+                  metainfo.json    # 物体身份、类别、mesh、尺寸与部件引用
+                  articulation.json
+                  keypoints/
+                  vertex_part_ids.pt
+                  joint_state.pt
+                  joint_state_valid.pt
+                  parts/<part_id>/pose.pt
+                  parts/<part_id>/pose_valid.pt
+              segment/
+                mask.mkv
+                mask_valid.pt
+                instances.jsonl
+              contact/
+                relations.jsonl
+                <subject_id>__<object_id>/  # 对应实体间的距离/接触区域
+```
+
+目录展示可能存在的组件，不要求填满。具体文件、字段和缺省规则分别见后续对应小节。原始包的解压发生在临时工作区，raw 内原始包保持不变。
+
+## 2. 相机视图的 metainfo.json
+
+### 2.1 顶层字段与发布信息
+
+每个相机视图必须有 `metainfo.json`。以下字段除另行说明外均为必填：
+
+| 字段 | 填写内容 |
+|---|---|
+| schema_version | 结构版本，本版为 `hand-warehouse/1.0` |
+| dataset_id | 数据集的逻辑标识 |
+| release_id | 本次数据发布版本，对应目录中的 dataset_version |
+| sequence_id | 所属序列或独立图片样本的标识 |
+| camera_id | 当前相机视图标识 |
+| sample_type | `image` 或 `video`，规则见 2.2 |
+| num_frames | 当前视图帧数 T，所有随帧组件按同一时间轴组织 |
+| hand_slots | 固定为 `["left", "right"]`；解剖学左右的使用规则见 5.1 |
+| sampling | 采样类型、源 FPS、帧索引引用及选择策略，见 2.2 |
+| camera | 当前相机模型与图像尺寸，见 2.3 |
+| components | 实际提供的组件及其文件、schema、有效性和来源，见 2.5 |
+| split | `train/val/test/unknown`，划分协议见 2.4 |
+| view_type | 必填，固定为 `ego/exo/unknown`。源数据或采集设置明确相机视角时必须填写 `ego` 或 `exo`；只有无法从可信来源确定时才填写 `unknown`，不得省略或主观猜测。多相机序列须逐视图分别填写。 |
+
+`dataset_version` 是数据发布版本；`schema_version` 是结构版本，两者独立。原始包校验摘要、官方来源和转换配置应记录在发布清单中。解压发生在临时工作区，原始包保持不变。
+
+### 2.2 图片、视频与 sampling
+
+`sample_type=image`：允许单图 T=1、`sampling.type=single`，也允许将独立图片批量装入一个视频容器，T>1 时 `sampling.type=independent`、`source_fps=null`。分片大小按数据集选择；FreiHAND 默认每片最多 4096 张，训练与评估分开，尾片不补帧。该数量不是其他数据集的强制要求；同批分辨率、相机模型类型及划分协议一致。每行 frames 必须有稳定且唯一的 `sample_id`、原始文件定位；内参、手形状等仍逐帧保留，不能因打包而共享错误的标定或身份。`sequence_id` 在这里仅是批次标识，不代表真实序列；不得按相邻帧推断跟踪、同步或接触延续。源 camera_id、image_variant、split 等随样本变化时放在 frames 的对应字段中。真实采集时间未知则 timestamp_ns=null。合成背景变体不视为真实多相机。
+
+`sample_type=video`：`sampling.type=dense/sparse`；`source_fps` 是真实采集 FPS，未知为 null。非连续取样必须保留源帧号和已知源时间，不可用采样后索引除以 FPS 冒充源时间。可另记采样策略与步长。
+
+shared 可以仅存所选 RGB 帧对应的数据。OakInk2 图像/动捕不同采样率时用 annotation_matches 明确映射；不要求将未采样动捕点硬塞进 T。多视角身份和源同步关系要保留；选择了哪些帧/相机及原因写在 sampling 中。
+
+`sampling.frames_ref` 指向 `camera/frames.jsonl`。帧号、逐帧时间、同步与标注匹配记录的结构见 4.1；MKV 封装时间的约定见 3.1。
+
+### 2.3 camera 模型声明
+
+`camera` 至少包含 `model`、`schema_id`、`image_size:[W,H]`。模型未知使用 `unknown` 和 `null`，不猜成针孔。实际内参、畸变及外参数组放在 `camera/`，各模型的固定结构见第 4 节。
+
+### 2.4 split 与划分协议
+
+保留上游划分，不将同一采集序列的相邻帧随意拆到不同集合。
+
+有官方划分时必填 split_scheme 和 split_source_locator，split_scheme 区分如 Dex-YCB s0/s1、ARCTIC P1/P2 等选定协议；不知道时 split=unknown 并给理由，不凭空分配。每发布可选择一种协议；存在逐帧差异时 frames 中的 split 覆盖视图默认值，并登记其来源。
+
+### 2.5 components 组件登记
+
+组件表的键是本发布内唯一名称，例如 `rgb`、`depth`、`mano`、`keypoints_uv`。每个已提供组件声明以下信息；模态特有参数在第 3–5 节对应小节中定义。
+
+| 字段 | 约定 |
+|---|---|
+| available | 本发布是否提供该组件；缺省也表示未提供 |
+| schema_id | 已登记的具体存储与语义类型，不得临时创造未登记名称 |
+| data_ref | 数据文件路径，相对当前相机视图根目录，使用 POSIX 路径 |
+| axes | 数组各维度的含义 |
+| valid_ref / valid_axes | 有效性文件及其维度；语义由所属组件规定 |
+| storage / valid_storage | 视频数据／视频有效性的存储配置；包含已登记 profile、实际 codec、pixel_format、encoded_size，见 3.1；非视频组件按原数组规则 |
+| provenance | source / format_conversion / derived，以及转换或生成方法 |
+| source_locator | 原文件、压缩包成员和记录／帧的定位信息 |
+
+不存在的组件允许缺省或写作 `{ "available": false, "reason": "..." }`，不用引用不存在的文件。静态网格可声明标量有效性；具体见 5.4。观测的 valid 见第 3 节，标注的 valid 与 visibility 区分见 5.1。
+
+`provenance.kind` 使用 source/format_conversion/derived，`method` 说明转换或生成方法；发布清单补充脚本版本、参数、输入摘要。`source_locator` 定位到原始文件/压缩包成员和记录、帧号；允许引用有完整溯源清单的前一发布，但链条最终应能回到 raw。不得仅写一个无法定位记录的数据集名字。
+
+`.pt` 仅保存 CPU Tensor，不保存 Python 自定义对象；浮点默认 float32，有效性为 bool，浮点无效位置填 NaN。所有随帧数组保留首维 T，单帧也不 squeeze；静态资产按其 schema 单独声明。单位、坐标及固定形状见各组件小节。
+
+机器注册表 `schema.json` 中，`components` 固定解码后的数据类型、形状、单位和语义；`storage_profiles` 固定视频内的编码／还原方式。不得临时创造未登记的 schema_id 或 storage.profile。同一语义允许选择其登记的不同无损存储 profile。
+
+## 3. observation 观测数据
+
+### 3.1 rgb.mkv 与 rgb_valid.pt
+
+RGB、深度和栅格 mask 在 shared 中统一使用 MKV 视频容器，不展开为逐帧图片或图像 tar。MANO、关键点、位姿等数值标注仍用数组；分割的重叠多边形仍用 JSONL，不为了视频化而有损栅格化。单图可为一帧视频，独立图批量封装规则见 2.2。
+
+**无损指共享层不增加信息损失。** 源 JPEG／H.264 等本身可能已是有损编码，原样保留并不恢复已丢失信息；不得重新以有损 JPEG、H.264、H.265、AV1 参数编码。源视频／图片的色彩、位深、分辨率和几何关系不得被静默改变。RGB schema `rgb/uint8/v1` 的统一读取结果是 `[T,H,W,3] uint8`、RGB 通道，有效性仍为 `rgb_valid.pt [T] bool`。超出该 schema 的高位深或 HDR 源必须报告并扩展定义，不能降到 8 位冒充无损。
+
+#### 3.1.1 允许的 RGB 存储 profile
+
+| storage.profile | 编码方式 | 适用规则 |
+|---|---|---|
+| mkv/mjpeg_copy/v1 | 源 JPEG 压缩帧直接封装成 MJPEG/MKV | 不解码重编码；保持每张 JPEG 码流字节；普通 8 位 JPEG 且 MKV/解码器支持时使用 |
+| mkv/source_copy/v1 | 已有视频的 RGB 对应码流直接封装为 MKV | 仅源编码能被 MKV 和选定解码器支持时使用，不伪造帧；需维持正确的呈现顺序与依赖帧 |
+| mkv/h264_rgb_lossless/v1 | libx264rgb 的真正无损模式 | 必须 RGB 路径、CRF=0，并通过全帧 RGB 字节比较；不能替换成默认 libx264 YUV420 路径 |
+| mkv/ffv1_rgb/v1 | FFV1 的 RGB 可逆编码路径 | 作为通用候选；解码回 RGB 后必须逐字节相同，不做色度降采样 |
+
+优先尝试源码流直接封装；不能封装或需要选择稀疏帧时，比较 FFV1 与无损 RGB H.264。对完整待发布片段，只在通过无损验收的候选中选择文件较小者，不假设某个编码器总最小；编码时间和随机读取代价记入构建记录。中间候选不作为 shared 多份常驻。H.264 跨帧压缩允许较长 GOP，必须记录 GOP／关键帧间隔，随机取帧需从前一关键帧正确解码，不能把 seek 返回的近似帧当目标帧。
+
+普通源视频可以保留它已有的有损码流，但不允许新增有损重编码。源是 VRS 等记录容器时，先查明图像流实际编码：已有兼容 JPEG／视频包可直接提取封装，否则解码后使用真正无损编码；不把整个 VRS 改后缀当 MKV。PNG 不要求直接塞进 MKV，可选上表两种无损重编码方式。
+
+#### 3.1.2 视频配置与校验
+
+每个视频组件在 metainfo 中登记 `storage`，至少包含：
+
+| 字段 | 内容 |
+|---|---|
+| profile | 上述登记的存储 profile；语义仍由组件 schema_id 指定 |
+| codec | 文件中实际 codec，例如 mjpeg、h264、ffv1；source_copy 必须写实际 codec |
+| pixel_format | 实际编码像素格式；不得仅凭编码器参数猜测 |
+| encoded_size | 视频物理像素尺寸 `[width,height]`；深度可能不同于逻辑尺寸，见 3.2 |
+| decoder | 解码器名称、版本，以及颜色矩阵／范围／旋转处理等实际像素解释配置；未知的源色彩声明 unknown，不擅自称为 sRGB |
+| encoder_parameters | 重编码时的编码器、版本、无损模式和 GOP 等参数；直接封装时记录 stream_copy |
+| source_lossy | 源是否已有损：true / false / unknown；不是共享层再有损编码的许可 |
+
+无损重新编码的 RGB 必须使用同一固定解码配置，比较**全部帧**的统一 RGB 字节、数量及顺序。MJPEG-copy 不重新编码，生产校验采用 `jpeg-packets-all-rgb-sample16/v1`：从 MKV 逐包取回，对全部源 JPEG 的 SHA256、数量、顺序和图像尺寸进行精确核对；每视图另用同一固定解码配置比较最多 16 帧的源与目标 RGB 字节（含首尾与四分位位置，其余由固定种子抽取）。记录策略、实际索引、种子与结果；不足 16 帧时全查。试点与最终完整序列随机抽查，对选中视图的全部帧复核解码 RGB。历史已做全帧像素检查的证明继续保留。
+
+逐包一致与像素抽查的范围必须分别声明，不能将抽查写成全量像素检查。不能用 PSNR 高、视觉相似、编码器参数称为 lossless 等替代精确检查。同时保留源编码的色彩标记和必要元数据；原图有 EXIF/色彩/方向等元数据时保留 raw 和必要来源信息；不能静默自动旋转图像而不更新像素对应与标定。以上加速策略仅适用于原 JPEG 码流直接封装，不适用于深度、标签或重新编码的 RGB。
+
+所有共享视频的呈现帧顺序与统一 frame_index 一一对应，每个栅格组件恰好 T 帧。MKV 的 `storage_fps` 只服务封装，声明 `timebase_role=index_only`；source_copy 可以保留源 PTS 以避免改坏码流，但真实时间与标注对齐仍以 frames 为准。未知真实时间保留 null。缺失某帧组件时保留索引位置并使用该组件的无效占位／valid 标识，不删帧；不能以重复相邻图像冒充已知观测。
+
+### 3.2 depth.mkv 与 depth_valid.mkv
+
+深度语义仍是 `depth/z_float32/v1`：统一读取结果 `[T,H,W] float32`，米制相机 Z 分量，`depth_type=z`。有效深度有限且大于 0，无效位置为 NaN。`depth_origin=measured/rendered/unknown` 必填，来源不明时 unknown。默认与 RGB 共用像素网格及相机参考系，不处理未注册 RGB-D。射线距离必须依据源定义转换成 z，不能只改声明。
+
+#### 3.2.1 float32 的可逆视频编码
+
+采用 `mkv/depth_float32_bits/v1`：**不是把深度量化成 uint16**，而是将规范化后每个 IEEE-754 float32 的完整 32 位拆成两个 uint16，使用 FFV1 gray16le 保存。视频物理尺寸为 `[2W,H]`：每行前 W 列存原 float32 位串的低 16 位，后 W 列存高 16 位。读取时先合并为 uint32 位串，再按 float32 重新解释，不能数值 cast 成 float32。
+
+```text
+u = reinterpret_uint32(depth_float32)
+encoded[:, :, :W] = uint16(u & 65535)
+encoded[:, :, W:] = uint16(u >> 16)
+u_restored = uint32(low) | (uint32(high) << 16)
+depth_float32 = reinterpret_float32(u_restored)
+```
+
+统一采用 little-endian 原始字节接口。`camera.image_size` 始终是逻辑 RGB 尺寸 `[W,H]`，不能改成编码视频的 2W；`storage.encoded_size=[2W,H]`。包括 NaN 在内的 float32 位串必须全帧完全恢复，不允许改成 float16、整数毫米、归一化灰度或 RGB 伪彩色。这里的无损验收基准是完成既定米制／坐标转换后的标准 float32 数组，不声称消除 float64→float32 等原约定转换本身的舍入。
+
+#### 3.2.2 逐像素有效性
+
+`depth_valid.mkv` 使用 `mkv/bool_ffv1/v1`，FFV1 gray8（FFmpeg pixel_format=`gray`），物理尺寸 `[W,H]`。像素只能为 0 或 1，统一解码为 `[T,H,W] bool`，逐像素含义不变。这样不再把庞大的 bool 数组保存为未压缩 `.pt`；标注的帧级／手级 valid 仍保留原有小数组。
+
+登记示例（W、H 以下取 640、480；decoder/encoder_parameters 的实际版本由转换器填写）：
+
+```json
+{
+  "available": true,
+  "schema_id": "depth/z_float32/v1",
+  "data_ref": "observation/depth.mkv",
+  "axes": ["T", "height", "width"],
+  "valid_ref": "observation/depth_valid.mkv",
+  "valid_axes": ["T", "height", "width"],
+  "unit": "m",
+  "depth_type": "z",
+  "depth_origin": "measured",
+  "invalid_value": "NaN",
+  "storage": {
+    "profile": "mkv/depth_float32_bits/v1",
+    "codec": "ffv1",
+    "pixel_format": "gray16le",
+    "encoded_size": [1280, 480],
+    "source_lossy": false,
+    "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+    "encoder_parameters": {"name": "ffv1", "level": 3, "version": "填写实际版本"}
+  },
+  "valid_storage": {
+    "profile": "mkv/bool_ffv1/v1",
+    "codec": "ffv1",
+    "pixel_format": "gray",
+    "encoded_size": [640, 480],
+    "source_lossy": false,
+    "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+    "encoder_parameters": {"name": "ffv1", "version": "填写实际版本"}
+  },
+  "provenance": {"kind": "format_conversion", "method": "源深度转米制 z float32；逐位打包为 FFV1"},
+  "source_locator": {"file": "raw/data/depth.zip", "member": "depth/0001.png"}
+}
+```
+
+深度和有效性视频都必须逐帧解码验收：float32 位串相同、bool 相同、T 相同，并检查 valid=true 的正有限值和 valid=false 的 NaN。读取器必须按 profile 还原，不能把 depth.mkv 的编码像素直接当深度值。
+
+## 4. camera 相机、帧索引与标定
+
+### 4.1 frames.jsonl：索引、时间与对应关系
+
+`camera/frames.jsonl` 每行对应统一帧号 `frame_index=0..T-1`，是所有组件的唯一行索引。至少包含 `frame_index`、`timestamp_ns`、`clock_domain`、`source_locator`；原始帧号/文件名保存在 `source_frame_id`，不能被新帧号替代。每行还可有 `annotation_matches`，按组件键保存 `source_frame_id`、`source_timestamp_ns`、`clock_domain`、`match_method` 和 `delta_ns`；方法为 exact/index_map/nearest/interpolated，索引映射无时间依据时 delta_ns=null。nearest 必须记录允许误差；interpolated 必须记录两端源标识和权重。delta_ns 仅在同一时间域中计算为源标注时间减图像时间。多相机共享 `sequence_id`；已知同步关系必须用 `sync_id` 或明确的源帧/时间映射表达，不能仅凭相同数组索引认定同步。
+
+每个视图必须提供此文件。源时间使用整数纳秒及 `clock_domain`，未知写 JSON `null`；不同时间域不能直接比较。逐帧 split 可以覆盖 metainfo 的视图默认划分，并保留来源，见 2.4。
+
+### 4.2 坐标系与标定通则
+
+| 项目 | 统一约定 |
+|---|---|
+| 相机坐标 | 右手系，X 向右、Y 向下、Z 向前，长度单位米 |
+| 齐次变换 | 列向量，`p_target = T_target_from_source @ p_source` |
+| 图像坐标 | `(u,v)`；左上像素中心为 `(0,0)`，u 向右、v 向下，单位 pixel |
+| 固定标定 | 本版展开为 T 份，同一 schema 不混用静态与动态形状 |
+| 分辨率或模型变化 | 分开存储为视图／片段，不在同一组件中混装 |
+| 未知标定 | 相机内外参允许缺省，不填单位阵冒充已知标定 |
+
+### 4.3 intrinsic.pt 与畸变参数
+
+| model / schema_id | intrinsic.pt 的固定结构 |
+|---|---|
+| pinhole / camera/pinhole/v1 | `[T,3,3]`，标准 K，最后一行为 `[0,0,1]`；限无畸变或已校正图像 |
+| fisheye624 / camera/fisheye624/v1 | `[T,15]`，`[f,cx,cy,k0,k1,k2,k3,k4,k5,p0,p1,s0,s1,s2,s3]`，采用 Project Aria Fisheye624 投影定义 |
+| opencv_fisheye4 / camera/opencv_fisheye4/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件 `distortion_ref` 指向 `[T,4]`，顺序 `[k1,k2,k3,k4]`，使用 OpenCV fisheye 等距角度多项式；已由 ReInterHand 实际原始相机参数验证 |
+| rational8 / camera/rational8/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件必需 `distortion_ref` 指向 `[T,8]`，顺序 `[k1,k2,p1,p2,k3,k4,k5,k6]` |
+| unknown / null | 不提供可用 intrinsic，不声称可以投影 |
+
+有畸变的其他模型必须注册新 schema，固定参数名、顺序和投影公式；不能塞进针孔 K 后遗失畸变项。`metainfo.camera.schema_id` 必须与 intrinsic 组件一致。同一模型的别名不可隐式改变参数布局。
+
+`opencv_fisheye4`：对前方点令 `x=X/Z, y=Y/Z, r=sqrt(x²+y²), theta=atan(r)`，`theta_d=theta*(1+k1*theta²+k2*theta⁴+k3*theta⁶+k4*theta⁸)`；`u=fx*x*theta_d/r+cx, v=fy*y*theta_d/r+cy`。光轴 r=0 时使用连续极限 `theta_d/r=1`。四系数不能按普通径向畸变解释，也不能补零冒充 Fisheye624。
+
+rational8 对归一化坐标 x=X/Z、y=Y/Z，令 r²=x²+y²，径向项为 `(1+k1*r²+k2*r⁴+k3*r⁶)/(1+k4*r²+k5*r⁴+k6*r⁶)`；再加 p1/p2 切向项并经 K 得到像素。`distortion_ref` 与 intrinsic 共用 `[T]` valid，保存到 camera/distortion.pt。不得把这种畸变三维坐标当作真实相机系 xyz 保存。
+
+内参有效性为 `intrinsic_valid.pt [T] bool`；有畸变时使用对应 `distortion_ref`。同一模型的参数名和顺序固定。
+
+### 4.4 extrinsic.pt 与 extrinsic_valid.pt
+
+`extrinsic.pt` 为 `[T,4,4]`，schema `transform/se3/v1`，方向固定 `T_camera_from_world`，有效性 `[T]`。世界坐标含义及源坐标转换记录在 provenance 中；只有相机坐标标注不要求虚构世界坐标。旋转块须正交且 determinant≈1。
+
+## 5. annotation 标注数据
+
+### 5.1 标注通则：手槽、valid 与 visibility
+
+手槽固定为 0=left、1=right，指解剖学左右，不按画面位置或可见性重排。本阶段只处理左右手两槽，不处理更多手。实体引用使用 `hand_left`、`hand_right`；手槽不证明跨采集片段的个人身份。
+
+#### 5.1.1 valid：标注是否可用于监督
+
+标注的 `valid=true` 表示：有对应实际标注、通过本 schema 数值检查，且通过来源明确提供并适用于该组件的有效性/QA 条件。`valid=false` 可由缺标注、无法建立帧对应、源无效占位或官方 QA 不通过导致；不等于画面没有手或手不可见。各组件独立判断，MANO 按 `[T,2]` 标记左右手，关键点按 `[T,2,K]` 标记。
+
+**2026-09-20 将标注 valid 从“仅表示存在”修订为“可用于监督”，字段形状不变。** 公式为 `source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass`。没有官方 QA 时省略最后一项，不伪造 QA；不能把未知当成已通过已存在的 QA。适配器在组件 `valid_policy` 中记录具体规则、来源、聚合/广播作用域。缺失官方必需 QA 的记录须明确处理为未知或暂停验收，不能静默通过。无效浮点项仍填 NaN，原始数据保留在 raw；被排除原值可用于 doc 中明确标识的诊断渲染，不能冒充 shared 有效监督。
+
+本地筛选须依据明确的规则，并在 `valid_policy.local_excluded_frame_indices`、排除原因和正式报告中记录。单个可疑投影作为问题记录，不直接硬编码排除该帧。已确认的曝光异常须明确作用范围，不能把一个序列的“前两帧有问题”套到所有序列。RGB 编码完好与几何监督可用是两件事，允许保留原图及帧索引而屏蔽对应标注；未发现问题不等于完成全部人工审核，试点待确认时不能作为已验收发布使用。
+
+源字段必须按官方语义映射，不能直接按名字复制：
+
+| 来源 | 官方含义 | 映射原则 |
+|---|---|---|
+| [H2O](https://github.com/taeinkwon/h2odataset) 的手标注首位 | 0 未标注、1 已标注 | 可作为对应手标注存在性的依据，不推断未标注原因 |
+| [HOT3D](https://github.com/facebookresearch/hot3d#using-mask-files) 的 pose_available、visible、mask_qa_pass | 分别表达姿态存在、可见性、对画面中可见手物位姿的人工 QA | 逐手/物体记录决定存在性，再 AND 当前相机当前帧 QA；QA 是整帧聚合判定，保守排除该帧全部手物几何监督，不声称每个实体均有错误；源可见性基于位姿推导时，其自身 valid 同样受 QA 约束 |
+| [Dex-YCB](https://github.com/NVlabs/dex-ycb-toolkit#loading-dataset-and-visualizing-samples) 的全零 pose_m、全 -1 joints | 不可见的手或缺标注共用占位表示 | 对应组件 valid=false；不能从该值判定 visibility=false，也不能声称原因是动捕缺失 |
+| [ARCTIC](https://github.com/zc-alexfan/arctic/blob/master/docs/data/data_doc.md) 的 is_valid/left_valid/right_valid | 是否有效用于官方评估 | 核实每项筛选依据；不把可见性/评估选择直接等同 QA。本次原始 mano.npy 不含这些派生评估标记，按原记录与数值检查生成 valid，并声明没有额外官方 QA 输入 |
+
+源 valid=false 若代表评估筛选或其他条件，应检查实际标注和源约定后映射；不能一律清空坐标。反之，占位零或 -1 只有在源官方明确规定时才判为缺失，不泛化成所有数据的判断规则。
+
+`valid=true` 的标注应通过其 schema 的数值与形状检查；检查失败必须报告或修复，不能静默隐藏。QA 不通过应报告排除数量与原因，并保留可追溯信息；不能把这类无效称为缺标注。RGB、深度等观测有效性仍按各自 schema；手物 QA 不自动否定 RGB、静态相机内参或物体资产。相机外参是否过滤须依据其自身来源规则；HOT3D 本次保留源外参用于溯源和诊断，不能用其存在性证明 QA 失败帧的几何监督正确。
+
+#### 5.1.2 visibility：可见性属性
+
+附加质量属性只保留 `visibility`，存放在对应模态或标注集内，不设 quality 目录。同一份可见性仅存一次；uv 与 xyz 共用时引用同一个文件。
+
+visibility 通过所属组件 attributes 登记 data_ref、axes、dtype、visibility_type、meaning、provenance、source_locator；未知值通过属性 valid_ref 标识，全已知可声明 valid=true。比例与布尔值不相互强转，也不按任意阈值二值化。没有 MANO 时，整手可见性挂在 keypoints 组件，使用独立的 whole_hand_visibility.pt，明确 meaning=whole_hand，不与逐关键点可见性混用。
+
+`valid` 回答“这项标注能否用于监督”，`visibility` 回答“在当前图像中是否可见（或可见多少）”。不可见的手或关键点仍可有有效标注；不能因 visibility=0 就清空坐标。源没有 visibility 时缺省或标为未知，不由 valid 推断。
+
+| 情况 | 手标注 valid | visibility |
+|---|---|---|
+| RGB 已开始录制，动捕尚未开始，没有其他对应手标注 | false | 以独立源可见性标注为准；没有则未知 |
+| 手被遮挡，但源有标注且通过适用有效性检查 | true | 若源声明不可见，则 false 或源可见比例 |
+| 手可见且有对应标注，通过适用 QA | true | 若源有此信息，则 true 或源可见比例 |
+| 手可见，但该帧漏标或动捕中断 | false | 可以为 true，但必须有独立来源依据 |
+| 有手部参数，但官方 QA 明确不通过 | false | 不据此断言不可见；源可见比例若基于不可靠位姿，其属性 valid 也为 false |
+
+visibility 自己的 valid_ref 表示该可见性标注是否可用，不复用 MANO 或关键点坐标的 valid_ref；明确记录源存在性及适用的 QA 条件。上述例子不要求凭图像自动生成可见性。
+
+不设 quality 组件或目录，不另存拟合误差、曝光、截断比例等通用质量数组。适用的官方 QA 合并进对应 valid，在 `valid_policy` 记录来源与规则；正式校验报告保留原始 QA 时间区间及排除统计。原始 QA 文件仍在 raw，官方 split 保留。
+
+### 5.2 mano：参数与整手可见性
+
+MANO schema `mano/axis_angle61/v1`：`motion.pt [T,2,61]`，顺序为完整 16 关节 axis-angle 48、shape betas 10、全局平移 3。姿态使用弧度，平移米，位于当前相机系。有效性 `[T,2]`；缺少一侧整槽 NaN。PCA 必须先按对应模型展开，不能将 PCA 系数直接填入 axis-angle。
+
+`model_profile` 必须固定 MANO 版本、`flat_hand_mean=true`、左手 shapedirs 修正状态。平移表示模型输出顶点所加的全局平移，不是某个腕关节的坐标；源语义不同须转换。shape 无量纲。模型权重不作为共享数据附带上传。
+
+`mano/visibility.pt [T,2]` 保存源整手可见比例时使用 float32 [0,1]，`visibility_type=ratio`；只有实际采用 0–100 的百分比才除以100，不能只凭表头判断。HOT3D 的 `visibility_ratio[%]` 实值已经是0–1，官方读取器原样读取，必须保留原值。通过 MANO 组件 attributes 登记，使用独立有效性，不复用 motion_valid。
+
+手部共享表示仅保留 MANO 参数与选定关键点，不发布独立手表面几何。需要渲染或接触计算时由 MANO 参数生成，几何引用使用固定 MANO 模型配置与顶点顺序；不影响 object 下的物体模型和材质资产。
+
+### 5.3 keypoints：二维点、三维点与逐点可见性
+
+关键点独立于 MANO 参数存在；没有 motion 也可有关键点。`uv.pt [T,2,K,2]` 与 `xyz.pt [T,2,K,3]`，有效性 `[T,2,K]`，分别采用 `keypoints/uv/v1` 和 `keypoints/xyz/v1`。K 由 `keypoint_schema` 固定，不能在同一数组中逐帧变化；同体系统一顺序。数值使用像素坐标或米制相机系。
+
+| keypoint_schema | K 与固定索引 |
+|---|---|
+| mano21/v1 | 21；wrist 后依次 thumb/index/middle/ring/little，每指由近端至指尖 |
+| rhd21/v1 | 21；保持 wrist，然后五指由近端至指尖；原始每指 tip→palm 反序。只统一顺序，不宣称解剖学位置等价于 MANO |
+
+具体名称与源索引映射见 `schema.json`。当前只登记 MANO21 与 RHD21；不登记 MediaPipe、ReInterHand capture21 或 fit24 中间关键点。
+
+每个发布中的每个视图只提供一套标准手关键点，直接放在 keypoints 下，不设 annotation_set_id 字段或目录。uv 与 xyz 是这套关键点的二维与三维表示，不是两套标注。provenance 分别记录二者来自原始标注还是计算结果；不要求已有 2D 必须由当前 xyz 重新投影覆盖。
+
+ReInterHand 的 Original keypoints、Original fits keypoints 及带前臂关键点不纳入 shared；如需手关键点，从选定 MANO 参数生成 MANO21 并记录模型/指尖回归约定。其他数据集默认使用选定的官方关键点，不额外生成平行的 MANO 派生集合。后续主动更换生成方法或结果时发布新的 dataset_version，不在同一版本内增设方法分支。
+
+manual/projection/fitting 只记录在 provenance，不作为目录或类型分支。不得通过坐标为零、是否出画或 visibility 判断标注是否存在。派生标注仅在实际生成并通过数值检查后声明 valid=true；没有生成的项为 false，不伪造坐标。
+
+`keypoints/visibility.pt [T,2,K]` 保存源逐点可见性，布尔表示使用 `visibility_type=boolean`。uv 与 xyz 共用时引用同一份文件。整手比例属性没有 MANO 可挂载时使用 `whole_hand_visibility.pt [T,2]`，`meaning=whole_hand`；它与逐点可见性分开，通用登记规则见 5.1。
+
+不使用 annotation_set_id；ReInterHand 中间关键点集合不进入 shared，raw 保留来源文件。
+
+### 5.4 object：身份、资产、位姿与几何
+
+#### 5.4.1 metainfo.json：物体身份与跟踪作用域
+
+手用 `hand_left`、`hand_right` 及槽位 0、1 表示，不再另存 entities.json。物体身份登记在 `annotation/object/<object_id>/metainfo.json`，必填 `object_id`、`identity_scope`、`scope_id`、`source_locator`；可含 `category_id`、`source_object_id`、`mesh_ref`、`has_model`、部件引用。即使只有分割、没有 pose/mesh，也可只有这份物体元信息。
+
+`identity_scope` 为 frame/subsequence/sequence，`scope_id` 指定具体作用域；跨相机共享身份只在已有源对应证据时使用。VISOR 按官方 subsequence 与实体名关联时记录 `association_basis=official_subsequence_entity_name`，不能声称这是全视频物理实例跟踪。无需与 object_id 一一对应的标准 track_id；官方独立轨迹标识存在时保留为 source_track_id，并声明作用域。手槽不证明跨采集片段的个人身份。
+
+#### 5.4.2 assets：网格、材质与纹理
+
+资产属于物体，统一存放在 `annotation/object/<object_id>/` 内，可用 `assets/` 子目录组织网格、材质、纹理和部件资产。序列层不设共享 assets。所有引用采用视图根目录为基准；其中物体资产组件的 `data_ref` 与部件的 `mesh_ref` 采用相对于当前视图目录的 POSIX 路径，例如 `annotation/object/cup_01/assets/cup.obj`，且必须指向所属物体目录内的资产。OBJ 内部的材质、纹理引用遵循文件格式自身的相对路径规则，依赖文件一并保留。不同相机视图允许各自保存物体资产，不要求跨视图去重；下载物体时包含其资产依赖。
+
+网格 schema 为 `mesh/obj/v1`，顶点米制，静态有效性可直接声明标量 `valid`，或通过 `valid_ref` 指向标量 bool Tensor；不按 T 重复静态资产。由物体／部件通过引用关联，不仅凭文件名猜测。
+
+#### 5.4.3 pose.pt、pose_valid.pt 与 visibility.pt
+
+刚体 schema 为 `object/rigid/v1`，`pose.pt [T,4,4]` 固定为 `T_camera_from_object`，平移米制，有效性 `[T]`。物体坐标原点、轴向必须与 mesh 一致；旋转块为正交矩阵且 determinant≈1。
+
+`object/<id>/visibility.pt [T]` 保存源物体可见比例，float32 [0,1]，`visibility_type=ratio`。通过所属物体组件 attributes 登记，独立表达其有效性。
+
+#### 5.4.4 articulation.json、部件位姿与 joint_state.pt
+
+关节物体：schema `object/articulated/v1`，`articulation.json` 登记 `parts` 与 `joints`。每个 part 有 `part_id`、`parent_id`（根为 null）、`has_model`、`mesh_ref`；has_model=false 时 mesh_ref=null，结构节点允许没有网格。父链不得成环。每个 joint 有唯一 `joint_id`、`parent_part`、`child_part`、`type`；fixed 单位 null、revolute 弧度、prismatic 米。有源限位时保存 `limits:[lower,upper]`；没有时 null，不伪造上下限。部件姿态统一为 `T_camera_from_part [T,4,4]`，不与父局部变换混装。
+
+若提供 `joint_state.pt [T,J]`，必须登记固定 joint_order、轴在父部件系的表达、零位变换与正方向，valid 为 `[T,J]`；缺少这些运动学定义时只发布部件位姿与已有来源信息，不伪造可驱动的关节模型。关节值与部件位姿同时存在时需验证一致性。
+
+部件层级应声明语义：纯语义包含树允许 `joints=[]`，不将包含关系推断为机械关节。关节状态 schema 为 `object/joint_state/v1`；转动用弧度、移动用米，混合类型按 joint_order 对应的关节定义解释。
+
+#### 5.4.5 dimensions、几何关键点与顶点部件标签
+
+`dimensions.pt [T,3]` 采用 `object/dimensions/v1`，有效性为 `[T]`。
+
+物体/部件 `dimensions` 为 `[length,width,height]` 米，轴顺序与该 mesh 的局部坐标关联，登记 `dimensions_semantics` 和来源。HOI4D dimensions 与 mesh 的具体尺度关系未验证前标为 source_unspecified，保留源值并暂停依赖该关系的几何验收，不能默认为待施加缩放。需要施加缩放时明确 scale_applied_to_mesh，禁止重复应用。
+
+源物体关键点放 `object/<id>/keypoints/xyz.pt [T,K,3]`（相机系）或 `template_xyz.pt [K,3]`（物体系），schema 分别为 object_keypoints/camera/v1 和 object_keypoints/template/v1。明确 keypoint_schema、keypoint_names、单位及有效性；模板点配合 pose 可投影，无须重复生成全部帧。H2O 中心/角点/棱中点，以及 ARCTIC 部件点分别登记具体源顺序，不套用手的 21 点 schema。每个发布选择一套源几何点并记录选择，不另建方法分支。
+
+`vertex_part_ids.pt [V] int64` 绑定 `mesh_ref` 与 topology_id，登记整数到 part_id 的映射；未知用 -1。ARCTIC 不同拓扑的资产分别绑定，不能共用未经转换的顶点标签。部件层级中 is_instance/has_model 等源属性保留，几何文件仍全部归 object。
+
+### 5.5 segment：分割类别、编码与覆盖范围
+
+每个发布选择一套任务使用的分割表示，不建立方法／标注集分支。没有分割就不创建 segment 目录，不为其他模态创建空的实体表。
+
+| schema_id | 文件与数值语义 |
+|---|---|
+| segment/uint16_labels/v1 | mask.mkv；整数区域编号，通过 instances.jsonl 解释 |
+| segment/instance_polygons/v1 | polygons.jsonl；实例多边形，可重叠；当前不支持孔洞拓扑 |
+| segment/foreground_intensity/v1 | mask.mkv；0–255 前景强度，不是区域编号或经过校准的概率 |
+
+#### 5.5.1 uint16 标签图与 instances.jsonl
+
+组件声明 `segmentation_type=semantic/instance/part`；同一组件固定一种类型。`mask.mkv` 解码为 `[T,H,W] uint16`，schema `segment/uint16_labels/v1`，使用 `mkv/uint16_ffv1/v1`，FFV1 gray16le 无损；只有所有区域编码均 ≤255 时允许 gray8（pixel_format=gray），读取后转 uint16，数值不变。禁止调色板和有损编码。有效性 `[T]`，表示该帧有可用分割标注。数值 0 固定为 unassigned，65535 固定为 ignore，1–65534 为区域编码，不能直接等同 entity_id。
+
+`instances.jsonl` 每帧一行，包含 `frame_index` 与 `labels`。每条标签包含 `mask_value`、`category_id`、`entity_id`、`part_id`；无意义或未知字段为 null。semantic 允许 entity_id=null；instance 必须引用 hand_left/hand_right 或登记的 object_id；part 还须关联登记部件。源实例 ID 和其他属性分别保存在 `source_instance_id`、`source_attributes`。mask 中每个非保留编码必须有且仅有一个映射。
+
+FreiHAND 的试点按本任务需要，统一到手／背景类别，保留每个原生区域编码；不要求给原生细分区域猜测手指名称。需要细部件监督时，尚未核实的部件名称必须保持未知，不能因粗粒度手类别已核实便声称部件语义也已统一。
+
+#### 5.5.2 重叠实例多边形
+
+每发布选择一套任务使用的分割表示，不建立方法/标注集分支。VISOR 的实际试点存在重叠实例多边形，单层标签图不能无损表达。对此补充 `segment/instance_polygons/v1`：`segment/polygons.jsonl` 每帧包含 `frame_index` 和 `instances`，每实例包含 `mask_value` 与 `polygons`（列表，每个多边形为源图像像素坐标顶点序列，保留原始精度）。身份和类别仍由同一 `instances.jsonl` 映射，有效性仍为 `[T]`，通过 schema 区分物理编码。允许多实例重叠，不采用后画覆盖前画。消费者按需栅格化为独立实例二值 mask；不能声称合并的单层 mask 无损。
+
+#### 5.5.3 灰度前景强度
+
+ReInterHand 的官方渲染 mask 为双手合并的灰度前景掩码，实测存在 0–255 的中间值，不是左右手实例编号。登记 `segment/foreground_intensity/v1`：仍存 `segment/mask.mkv`，解码为 `[T,H,W] uint16`，使用 `mkv/uint16_ffv1/v1`，此类数据优先编码为 FFV1 gray8（pixel_format=gray），读取后转 uint16。有效值范围 0–255，归一化值为 `float32(value)/255`；0 为背景端点、255 为前景端点。组件声明 `foreground_category=hand`、`entity_id=null` 和双手合并语义，不使用区域编号映射。这里保留原始渲染 mask 强度，不把它称为经过校准的概率。若任务需要二值 mask，阈值化属于任务层派生操作，记录阈值；共享层不丢弃中间灰度。不同 schema 的数值含义不同，不能将该文件按整数类别图解码。
+
+FreiHAND 训练集的 JPEG mask 使用 `segment/foreground_intensity/v1`，逐像素保留 JPEG 解码后的灰度，`entity_id=hand_right`。该 schema 不限定双手合并：单手或合并范围由 `entity_id` 与 `coverage` 明确声明。评估集整数区域图仍用 `segment/uint16_labels/v1`，两者不能仅凭文件名使用同一种解码语义。
+
+#### 5.5.4 coverage 与有效性
+
+`coverage.kind=full/selected_entities/partial/unknown`。full 指声明的标注范围完整；selected_entities 还应列出目标范围。无论哪种，0 本身不自动成为背景；背景要有明确类别映射，未标注区域不能用于构造负样本。保留源逐类/逐实例 exhaustive 信息，未知不可改写为完整。
+
+三种表示的帧级有效性均为 `[T]`，表示该帧是否提供此分割标注；对应 valid_ref 在组件中登记，仍用小型 bool `.pt` 数组。栅格 mask 必须登记 storage，格式见 3.1.2，所有帧的整数编码或强度逐值完全相同。编码语义由 schema_id 决定，读取器不能仅依据 mask.mkv 文件名选择解码方式。
+
+### 5.6 contact：接触、抓取、区域与距离
+
+#### 5.6.1 relations.jsonl 与 relations_valid.pt
+
+`annotation/contact/relations.jsonl`，schema contact/relations/v1，每帧一行含 frame_index、relations。每条记录含 relation_type（contact/grasp）、subject_id（手槽或登记实体）、object_id、status、source_locator、provenance。status 固定为 positive/negative/unknown/target_unannotated：positive 必须有可解析目标，negative 表示源明确否定该关系，unknown 为不确定，target_unannotated 为确有接触但目标未标注。非 positive 的 object_id 可 null，不能用 null 冒充“不接触”。稀疏源无记录的帧不自动补 negative。
+
+VISOR 的对象 mask ID 必须先按同帧 source_instance_id 解析成 object_id；hand-not-in-contact、none-of-the-above、inconclusive 分别映射 negative、target_unannotated、unknown，保留原始字符串。Dex ycb_grasp_ind 通过 ycb_ids 映射到登记物体，以 relation_type=grasp 保存；不得当作逐顶点接触。
+
+relations 组件的 valid_ref 指向 `relations_valid.pt [T] bool`，表示该帧是否有可用源关系标注。有效帧中可以存在 status=unknown 的明确不确定标注；无源标注的帧为 valid=false、relations=[]。组件仍声明 available/provenance/source_locator。
+
+#### 5.6.2 实体对的顶点区域与距离
+
+已发布的接触区域/距离按参与关系的 subject_id 与 object_id 组织，每对实体保留选定的一套结果，以精确 geometry_ref/topology_id 关联到手或物体/部件；不要求每套数据都有。顶点区域 schema contact/vertex_region/v1：bool `[T,V]` 加同形 valid；顶点距离 schema contact/vertex_distance/v1：float32 `[T,V]`、米、同形 valid，声明 source/target geometry_ref、distance_definition（signed/unsigned、测量到点集或表面等明确源定义）。geometry_ref 必须指向已登记 mesh 或由固定 MANO 模型配置确定的拓扑；拓扑/顶点数变化时按拓扑拆分片段。其他源区域表示只有在实际接入并登记 schema 后才可转换，不能随意栅格化宣称等价。
+
+顶点区域／距离已有存储定义，但尚未完成真实输入试点；接入时必须提供源语义与几何验收证据，不因已经登记 schema 就视为通过。
+
+## 附录：任务范围
+
+不强制收录 2D bbox、完整高频动捕、动作程序、身体 SMPL-X、点云或网络特征。范围内的接触、visibility、选定手关键点、物体几何不能因不便处理而静默丢弃。分割采用的物理编码由第 5.5 节决定，存在重叠时不能以单层标签图声称无损。
+
+不设置 entities_ref/segment/entities.json，不要求与 object_id 重复的 track_id。MediaPipe 不在当前关键点定义中；旧版本迁移必须保留可追溯关系，不能直接删表。手部共享表示不含独立 hand_mesh。
+
+schema.json 是普通注册表，不是 JSON Schema 文档或可执行验收器；定义存在不代表真实数据已验证。验收须保留实测范围与未决项。
+
+本存储约定只更新标准，不代表既有 FFV1／depth.pt 试点已迁移。新发布必须显式写 storage 配置；旧产物按其旧定义读取，不能只改文件后缀或版本标识伪装为合规。
diff --git a/EgoHOS/shared-v1.0/doc/DATA_MAPPING.md b/EgoHOS/shared-v1.0/doc/DATA_MAPPING.md
new file mode 100644
index 0000000000000000000000000000000000000000..8b0af4fc8ea7a2fd3fac55afbf65d6df0390bb60
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/DATA_MAPPING.md
@@ -0,0 +1,42 @@
+# EgoHOS raw → shared-v1.0
+
+范围：RGB、官方九类语义分割。数据不是已验证连续视频；按独立图片打包，不推断跟踪、时间、相机标定、实例身份或接触延续。
+
+## 来源与实际输入
+
+- 仓库：<https://huggingface.co/datasets/ParityLab/Hand-Datasets-Raw/tree/main/EgoHOS/raw>
+- 固定 HF revision：`8818d04d6cbdc0bf32b6ef949257b82310f16798`。
+- 官方语义：<https://github.com/owenzlz/EgoHOS/blob/cd9bdf427ef1d94582334ebba4188d5aa18433e7/README.md>。
+- 外层 `egohos_dataset.zip` 同时包含内层 ZIP 与解压副本；转换只取外层的 image/label，避免重复收录。
+- 四组原始图片数：train 8993、val 1124、test_indomain 1126、test_outdomain 500。来源是仓库 raw/doc/SOURCES.md；全量脚本另做实际计数校验。
+- 试点 HTTP Range 只取中央目录及选中成员，逐成员 CRC、SHA256 校验。没有下载全包时，不声称验证了整个 ZIP 的 SHA256。
+
+## 字段映射
+
+| raw | shared | 规则 |
+|---|---|---|
+| `<split>/image/<name>` | `observation/rgb.mkv` | JPEG 优先原码流 remux，所有包 SHA256 和全部解码 RGB 核对；不适用时同时验证 FFV1 / libx264rgb CRF=0，取完整视图文件较小者 |
+| RGB 文件可正常解码 | `rgb_valid.pt [T] bool` | 全部 true；坏文件立即失败，不跳过、不补邻帧 |
+| `<split>/label/<stem>.png` | `annotation/segment/mask.mkv` | 保留索引值，不将调色板 RGB 当标签；FFV1 gray16le，全帧逐整数核对 |
+| 类别 0 background | shared mask 9 | shared 0 保留给 unassigned，显式背景不能继续占用 0 |
+| 类别 1、2 | shared mask 1、2 | 解剖学 left/right，entity_id=hand_left/hand_right |
+| 类别 3、4、5 | shared mask 3、4、5 | 左/右/双手一级交互物体类别；entity_id=null |
+| 类别 6、7、8 | shared mask 6、7、8 | 左/右/双手二级交互物体类别；entity_id=null |
+| 以上类别表 | `instances.jsonl` | 每帧一行，semantic 类型；无实例/部件 ID，不捏造实例跟踪 |
+| 源 label 存在且类别/尺寸合法 | `mask_valid.pt [T] bool` | 无额外官方 QA 输入；不能用 mask valid 推断手可见性 |
+| 目录 split | metainfo + frames.split | 两个 test 目录均为 test，source_split 完整区分 indomain/outdomain |
+| 原文件名、归档路径 | `camera/frames.jsonl` | 稳定 sample_id、源 member、源 SHA256、revision、逐帧原定位 |
+| 真实时间/标定缺失 | null / component unavailable | camera model=unknown；不伪造 K、单位外参、FPS |
+
+按 split 和分辨率分组，每片最多 `--chunk` 张。T=1 为 image/single，T>1 为 image/independent；所有 timestamp_ns/clock_domain/source_fps 为 null。MKV 1 FPS 仅作索引，不是采集帧率。view_type=ego。
+
+## 未纳入内容及原因
+
+- contact：官方 README 描述 contact/，但 ParityLab 原始包实际上没有此目录；标注为缺失，不能把交互物体类别推断成逐实体 positive/negative 接触。
+- 几何、MANO、深度、内外参、visibility：该输入无对应监督，缺省，不生成空几何或虚构单位阵。
+- 模型训练源码、权重、演示 GIF：不是观测或标注；原始包和源码仍在 raw。
+- semantic 的 task background 不是所有物体的完备负样本，coverage=partial。
+
+## 验收
+
+`encoding_validation.json` 是每视图逐帧字节验收证明；`doc/validation/report.json` 记录自动检查范围、HTML 样本和全发布文件 SHA256。HTML 展示所有已提供模态及缺失原因。只有人工查看 HTML 后才运行 approve；自动测试不替代人工审核。
diff --git a/EgoHOS/shared-v1.0/doc/RAW_TO_SHARED.md b/EgoHOS/shared-v1.0/doc/RAW_TO_SHARED.md
new file mode 100644
index 0000000000000000000000000000000000000000..8b0af4fc8ea7a2fd3fac55afbf65d6df0390bb60
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/RAW_TO_SHARED.md
@@ -0,0 +1,42 @@
+# EgoHOS raw → shared-v1.0
+
+范围：RGB、官方九类语义分割。数据不是已验证连续视频；按独立图片打包，不推断跟踪、时间、相机标定、实例身份或接触延续。
+
+## 来源与实际输入
+
+- 仓库：<https://huggingface.co/datasets/ParityLab/Hand-Datasets-Raw/tree/main/EgoHOS/raw>
+- 固定 HF revision：`8818d04d6cbdc0bf32b6ef949257b82310f16798`。
+- 官方语义：<https://github.com/owenzlz/EgoHOS/blob/cd9bdf427ef1d94582334ebba4188d5aa18433e7/README.md>。
+- 外层 `egohos_dataset.zip` 同时包含内层 ZIP 与解压副本；转换只取外层的 image/label，避免重复收录。
+- 四组原始图片数：train 8993、val 1124、test_indomain 1126、test_outdomain 500。来源是仓库 raw/doc/SOURCES.md；全量脚本另做实际计数校验。
+- 试点 HTTP Range 只取中央目录及选中成员，逐成员 CRC、SHA256 校验。没有下载全包时，不声称验证了整个 ZIP 的 SHA256。
+
+## 字段映射
+
+| raw | shared | 规则 |
+|---|---|---|
+| `<split>/image/<name>` | `observation/rgb.mkv` | JPEG 优先原码流 remux，所有包 SHA256 和全部解码 RGB 核对；不适用时同时验证 FFV1 / libx264rgb CRF=0，取完整视图文件较小者 |
+| RGB 文件可正常解码 | `rgb_valid.pt [T] bool` | 全部 true；坏文件立即失败，不跳过、不补邻帧 |
+| `<split>/label/<stem>.png` | `annotation/segment/mask.mkv` | 保留索引值，不将调色板 RGB 当标签；FFV1 gray16le，全帧逐整数核对 |
+| 类别 0 background | shared mask 9 | shared 0 保留给 unassigned，显式背景不能继续占用 0 |
+| 类别 1、2 | shared mask 1、2 | 解剖学 left/right，entity_id=hand_left/hand_right |
+| 类别 3、4、5 | shared mask 3、4、5 | 左/右/双手一级交互物体类别；entity_id=null |
+| 类别 6、7、8 | shared mask 6、7、8 | 左/右/双手二级交互物体类别；entity_id=null |
+| 以上类别表 | `instances.jsonl` | 每帧一行，semantic 类型；无实例/部件 ID，不捏造实例跟踪 |
+| 源 label 存在且类别/尺寸合法 | `mask_valid.pt [T] bool` | 无额外官方 QA 输入；不能用 mask valid 推断手可见性 |
+| 目录 split | metainfo + frames.split | 两个 test 目录均为 test，source_split 完整区分 indomain/outdomain |
+| 原文件名、归档路径 | `camera/frames.jsonl` | 稳定 sample_id、源 member、源 SHA256、revision、逐帧原定位 |
+| 真实时间/标定缺失 | null / component unavailable | camera model=unknown；不伪造 K、单位外参、FPS |
+
+按 split 和分辨率分组，每片最多 `--chunk` 张。T=1 为 image/single，T>1 为 image/independent；所有 timestamp_ns/clock_domain/source_fps 为 null。MKV 1 FPS 仅作索引，不是采集帧率。view_type=ego。
+
+## 未纳入内容及原因
+
+- contact：官方 README 描述 contact/，但 ParityLab 原始包实际上没有此目录；标注为缺失，不能把交互物体类别推断成逐实体 positive/negative 接触。
+- 几何、MANO、深度、内外参、visibility：该输入无对应监督，缺省，不生成空几何或虚构单位阵。
+- 模型训练源码、权重、演示 GIF：不是观测或标注；原始包和源码仍在 raw。
+- semantic 的 task background 不是所有物体的完备负样本，coverage=partial。
+
+## 验收
+
+`encoding_validation.json` 是每视图逐帧字节验收证明；`doc/validation/report.json` 记录自动检查范围、HTML 样本和全发布文件 SHA256。HTML 展示所有已提供模态及缺失原因。只有人工查看 HTML 后才运行 approve；自动测试不替代人工审核。
diff --git a/EgoHOS/shared-v1.0/doc/RUNBOOK.md b/EgoHOS/shared-v1.0/doc/RUNBOOK.md
new file mode 100644
index 0000000000000000000000000000000000000000..c18da834eac5843bd9b04b38bd8f415159c1cddb
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/RUNBOOK.md
@@ -0,0 +1,147 @@
+# EgoHOS / OakInk-v1 预处理脚本
+
+以随包 CONTRACT.md 与 schema.json 为规范，输出 `<数据集>/shared-v1.0/{doc,script,data}`。默认只跑试点。**人工查看试点 HTML 后才能全量；全量结束后再次抽样人工确认才能上传。** 本包不是全量处理结果。
+
+## 当前覆盖范围
+
+| 数据集 | 可用路径 | 待补充 |
+|---|---|---|
+| EgoHOS | RGB + 9 类语义分割；四个官方 split；真实源样本可直接 Range 下载试点 | raw 包没有独立 contact 图，无标定/几何 |
+| OakInk-v1 / OakInk-Image | RGB-only 明确选项；可在服务器处理图像分卷 | 当前 HF raw 不含 anno_v2_1.zip；几何分支须补齐后真实试点 |
+| OakInk 几何可选分支 | K、外参、相机系右手 xyz/派生 uv、物体位姿、居中 OBJ 与依赖 | 无真实标注验收前，仅视为待验证适配器；MANO 未实现，不伪造参数 |
+
+OakInk-Shape/OakBase 不作为图像帧转换；具体排除原因见 DATA_MAPPING 文档。FreiHAND 参考链接本次匿名访问返回 401，未以未读内容作实现依据；目录和字段按用户提供的两个约定文件实现。
+
+## 环境
+
+Python 3.10+、FFmpeg（须有 ffv1 与 libx264rgb）、OakInk 分卷解压需要 7z/7zz。CPU 即可。
+
+```bash
+python3 -m venv .venv
+. .venv/bin/activate
+python -m pip install -r requirements.txt
+python test_self.py
+```
+
+如只用 CPU，可先按 PyTorch 官方 CPU 索引安装 torch 再安装 requirements，避免下载 CUDA 依赖。HF 下载/上传使用本机 `hf auth login` 或 HF_TOKEN；不要把 token 写入脚本或发布目录。
+
+## 1. EgoHOS 真实小试点（不下载 4.5 GB 全包）
+
+以下命令在本脚本目录运行，所有输出路径可改到大容量磁盘。extracted 是工作区；不在 raw 内解压。
+
+```bash
+python acquire.py pilot-egohos --out /storage/work/egohos-pilot --per-split 4
+python preprocess.py build --dataset EgoHOS \
+  --input /storage/work/egohos-pilot --out /storage/pilot/EgoHOS/shared-v1.0
+```
+
+浏览 `/storage/pilot/EgoHOS/shared-v1.0/doc/validation/index.html`（静态 HTML，可直接打开）。下拉框能逐帧查看全部试点样本、源 RGB、shared 解码 RGB、mask、叠加、valid、来源、元信息和缺失原因。请核对左右手、背景编码、细物体边界、四个 split。**只有人实际看过并接受后执行：**
+
+```bash
+python preprocess.py approve --release /storage/pilot/EgoHOS/shared-v1.0 --reviewer '审核者姓名'
+```
+
+approve 是人工声明记录，不会自行判断人工是否看过。程序不会自动调用 approve。
+
+## 2. 原始包下载与工作区解压
+
+```bash
+python acquire.py download --dataset EgoHOS --raw /storage/EgoHOS/raw
+python acquire.py extract --dataset EgoHOS --raw /storage/EgoHOS/raw --out /storage/work/egohos-full
+
+python acquire.py download --dataset OakInk-v1 --raw /storage/OakInk-v1/raw
+python acquire.py extract --dataset OakInk-v1 --raw /storage/OakInk-v1/raw --out /storage/work/oakink-full
+```
+
+固定 HF commit，并校验来源文档给出的每包大小/SHA256；原始包保持不变。OakInk `.z01`–`.z10` 与最终 `.zip` 是分卷 ZIP，由 7z 从最终 .zip 读取，不用 `cat` 伪拼接。下载约 124 GB，还需解压、编码候选与输出空间；预先准备数百 GB 空间，命令做磁盘预检但不保证压缩率估计准确。提取失败的目录不自动覆盖，诊断后选新目录重跑。
+
+## 3. OakInk 试点
+
+无需先下载全部 124 GB，也可做有限范围 RGB 小试点（只从首个分卷的前 128 个 PNG 条目中按相机抽样，不代表全数据集随机覆盖）：
+
+```bash
+python acquire.py pilot-oakink-rgb --out /storage/work/oakink-pilot --per-split 2
+python acquire.py fetch-oakink-objects --out /storage/work/oakink-pilot
+python preprocess.py build --dataset OakInk-v1 --input /storage/work/oakink-pilot \
+  --out /storage/pilot/OakInk-v1/shared-v1.0 --allow-rgb-only
+```
+
+完整工作区已解压时，当前 HF 没有 anno，若本次范围只接受现有 RGB：
+
+```bash
+python preprocess.py build --dataset OakInk-v1 \
+  --input /storage/work/oakink-full --out /storage/pilot/OakInk-v1/shared-v1.0 \
+  --allow-rgb-only --per-stratum 4
+```
+
+按 camera×split 固定种子抽样，unknown split 明确保留，不随意划分训练集。该结果没有手关键点或手/物逐帧位姿；如果工作区已有 image/obj，会保留静态网格和规范坐标系预览，仍不能作为完整几何交付。
+
+若已获可信官方 `anno_v2_1.zip`，先用以下命令校验每成员 CRC、记录文件和原包 SHA256，再解压到同一工作区 `image/anno`：
+
+```bash
+python acquire.py extract-annotations --archive /storage/OakInk-v1/raw/data/image/anno_v2_1.zip \
+  --out /storage/work/oakink-full
+```
+
+该命令要求包内根目录为 anno/，发现不同布局会停止，避免猜错路径。然后运行：
+
+```bash
+python preprocess.py build --dataset OakInk-v1 \
+  --input /storage/work/oakink-full --out /storage/pilot/OakInk-v1/shared-v1.0 \
+  --annotations /storage/work/oakink-full/image/anno --trust-pickle --split-scheme split0
+```
+
+`--trust-pickle` 仅用于可信官方标注，因为 pickle 可执行代码。每条来源记录格式（路径相对 --input；SHA256 填真实结果）：
+
+```json
+{"path":"image/anno/seq_all.json","sha256":"实际文件SHA256","source_locator":{"file":"OakInk-v1/raw/data/image/anno_v2_1.zip","archive_sha256":"实际包SHA256","member":"anno/seq_all.json"}}
+```
+
+几何路径不会强行把 sbj_flag 0/1 映射成左右手；两人的右手分别在 person0/person1 序列。检查 canonical 网格、相机系关键点/物体点云以及 2D 叠加。模型来源、量纲或关键点定义无法确认时不要 approve。MANO 需要另做模型配置与顶点重建对照，当前不导出。
+
+## 4. 试点批准后全量
+
+```bash
+python preprocess.py build --dataset EgoHOS --phase full \
+  --input /storage/work/egohos-full --out /storage/EgoHOS/shared-v1.0 \
+  --pilot /storage/pilot/EgoHOS/shared-v1.0
+
+python preprocess.py approve --release /storage/pilot/OakInk-v1/shared-v1.0 --reviewer '审核者姓名'
+python preprocess.py build --dataset OakInk-v1 --phase full \
+  --input /storage/work/oakink-full --out /storage/OakInk-v1/shared-v1.0 \
+  --pilot /storage/pilot/OakInk-v1/shared-v1.0 --allow-rgb-only
+```
+
+OakInk 若试点启用标注，全量也用相同 --annotations、--trust-pickle、--split-scheme，去掉 --allow-rgb-only。chunk、seed、代码、模态范围须与试点一致；变更后须重做试点。输出目录必须不存在。EgoHOS full 强制核对四组完整数量，防止把试点数据当全量。
+
+所有视图均逐帧校验视频/数组；全量 HTML 按 split×camera 随机抽取视图并显示所选视图的每一帧，绝不将人工抽检称为全量人工审核。若要浏览所有帧的所有要素（会生成大量 PNG/HTML）：
+
+```bash
+python validate.py /storage/EgoHOS/shared-v1.0 --all --source-root /storage/work/egohos-full
+```
+
+重新 validate 会使旧人工审核失效。输入、输出、每帧 provenance 与文件 SHA256 均可检查。自动检查只验证实现支持的字段，并不能自动证明数据集原生语义和真实几何正确。
+
+## 5. 最终抽检与上传
+
+打开全量结果的 doc/validation/index.html，再次人工抽检后：
+
+```bash
+python preprocess.py approve --release /storage/EgoHOS/shared-v1.0 --reviewer '审核者姓名'
+python preprocess.py upload --release /storage/EgoHOS/shared-v1.0
+
+python preprocess.py approve --release /storage/OakInk-v1/shared-v1.0 --reviewer '审核者姓名'
+python preprocess.py upload --release /storage/OakInk-v1/shared-v1.0
+```
+
+目标按用户要求固定为 **ParityLab/Hand-Datasets-Raw**，分别是 `EgoHOS/shared-v1.0` 和 **大小写准确的** `OakInk-v1/shared-v1.0`。需要写权限；任何 pilot 都禁止作为正式 shared-v1.0 上传。上传前验证文件哈希，按最多 128 个文件分批提交、文档最后上传；拒绝合并到已经存在的同版本目录，防止留下旧文件。中途失败会保留已提交批次，脚本不会自动覆盖/删除，须核验远端后恢复。未自动上传、未代替用户签署人工审核。
+
+## 输出文件
+
+- `doc/DATA_MAPPING.md` 与兼容约定文件名 `doc/RAW_TO_SHARED.md`：raw 映射、未纳入内容和原因。
+- `doc/validation/index.html`、预览 PNG、`report.json`：视觉与自动检查。
+- `doc/build.json`：完整命令、源 inventory 摘要、选择策略、处理数。
+- `script/`：本次处理脚本和全部必需支持文件；不包含 venv、token 或 MANO 权重。
+- `data/<sequence>/<camera>/`：metainfo、frames、MKV、CPU tensor、已选物体资产。
+
+测试状态与真实试点范围见随包 `TEST_RESULTS.md`。没有真实标注包时，对几何分支的检查只能算合成端到端检查。
diff --git a/EgoHOS/shared-v1.0/doc/build.json b/EgoHOS/shared-v1.0/doc/build.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e86e5c0eb3dd91996c56890c9659375bd8773d8
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/build.json
@@ -0,0 +1,158 @@
+{
+  "dataset": "EgoHOS",
+  "phase": "full",
+  "recipe": {
+    "dataset": "EgoHOS",
+    "chunk": 128,
+    "seed": 20260928,
+    "annotations": false,
+    "split_scheme": "split0",
+    "code_sha256": {
+      "preprocess.py": "c5507d5ba4682f8a21ec77f4b9ff4af31fcf473eb98a95280e32b6ae9b9b5f20",
+      "media.py": "ace183f298d1073ee9d453f13464d58e0be685fe09a3ddd99a78c67b4c7337ec",
+      "validate.py": "3dc2bdadd89b03ca289673f40f45a724947972880dab6f537fb60354f90cc7b5",
+      "acquire.py": "1f010b87f6cc13d41058ba5b6b4ac89b4becc9ef4b5a15f887f00842dfdc6993",
+      "schema.json": "9a34b349ed9315fb0f6ccbe3425e852c971b5a4aaf26a094f4f4df15bfccc091",
+      "CONTRACT.md": "c34cdf6645f91233cc28d86f8abc82f019f78767d863e225f75722984d9adb19"
+    }
+  },
+  "inventory_samples": 11743,
+  "selected_samples": 11743,
+  "views": [
+    "data/test_indomain_1440x1080_000000/ego",
+    "data/test_indomain_1440x1080_000001/ego",
+    "data/test_indomain_1440x1080_000002/ego",
+    "data/test_indomain_1920x1080_000000/ego",
+    "data/test_indomain_1920x1440_000000/ego",
+    "data/test_indomain_1920x1440_000001/ego",
+    "data/test_indomain_2560x1440_000000/ego",
+    "data/test_indomain_2560x1920_000000/ego",
+    "data/test_indomain_342x256_000000/ego",
+    "data/test_indomain_3840x2160_000000/ego",
+    "data/test_indomain_456x256_000000/ego",
+    "data/test_indomain_456x256_000001/ego",
+    "data/test_indomain_640x480_000000/ego",
+    "data/test_outdomain_1920x1080_000000/ego",
+    "data/test_outdomain_1920x1080_000001/ego",
+    "data/test_outdomain_1920x1080_000002/ego",
+    "data/test_outdomain_1920x1080_000003/ego",
+    "data/train_1440x1080_000000/ego",
+    "data/train_1440x1080_000001/ego",
+    "data/train_1440x1080_000002/ego",
+    "data/train_1440x1080_000003/ego",
+    "data/train_1440x1080_000004/ego",
+    "data/train_1440x1080_000005/ego",
+    "data/train_1440x1080_000006/ego",
+    "data/train_1440x1080_000007/ego",
+    "data/train_1440x1080_000008/ego",
+    "data/train_1440x1080_000009/ego",
+    "data/train_1440x1080_000010/ego",
+    "data/train_1440x1080_000011/ego",
+    "data/train_1440x1080_000012/ego",
+    "data/train_1440x1080_000013/ego",
+    "data/train_1440x1080_000014/ego",
+    "data/train_1440x1080_000015/ego",
+    "data/train_1440x1080_000016/ego",
+    "data/train_1440x1080_000017/ego",
+    "data/train_1440x1080_000018/ego",
+    "data/train_1440x1080_000019/ego",
+    "data/train_1440x1080_000020/ego",
+    "data/train_1440x1080_000021/ego",
+    "data/train_1440x1080_000022/ego",
+    "data/train_1440x1080_000023/ego",
+    "data/train_1920x1080_000000/ego",
+    "data/train_1920x1080_000001/ego",
+    "data/train_1920x1080_000002/ego",
+    "data/train_1920x1080_000003/ego",
+    "data/train_1920x1080_000004/ego",
+    "data/train_1920x1080_000005/ego",
+    "data/train_1920x1080_000006/ego",
+    "data/train_1920x1440_000000/ego",
+    "data/train_1920x1440_000001/ego",
+    "data/train_1920x1440_000002/ego",
+    "data/train_1920x1440_000003/ego",
+    "data/train_1920x1440_000004/ego",
+    "data/train_1920x1440_000005/ego",
+    "data/train_1920x1440_000006/ego",
+    "data/train_1920x1440_000007/ego",
+    "data/train_1920x1440_000008/ego",
+    "data/train_1920x1440_000009/ego",
+    "data/train_1920x1440_000010/ego",
+    "data/train_1920x1440_000011/ego",
+    "data/train_1920x1440_000012/ego",
+    "data/train_1920x1440_000013/ego",
+    "data/train_1920x1440_000014/ego",
+    "data/train_1920x1440_000015/ego",
+    "data/train_2560x1440_000000/ego",
+    "data/train_2560x1920_000000/ego",
+    "data/train_2560x1920_000001/ego",
+    "data/train_2560x1920_000002/ego",
+    "data/train_2560x1920_000003/ego",
+    "data/train_2560x1920_000004/ego",
+    "data/train_2560x1920_000005/ego",
+    "data/train_342x256_000000/ego",
+    "data/train_3840x2160_000000/ego",
+    "data/train_3840x2160_000001/ego",
+    "data/train_456x256_000000/ego",
+    "data/train_456x256_000001/ego",
+    "data/train_456x256_000002/ego",
+    "data/train_456x256_000003/ego",
+    "data/train_456x256_000004/ego",
+    "data/train_456x256_000005/ego",
+    "data/train_456x256_000006/ego",
+    "data/train_456x256_000007/ego",
+    "data/train_456x256_000008/ego",
+    "data/train_456x256_000009/ego",
+    "data/train_456x256_000010/ego",
+    "data/train_456x256_000011/ego",
+    "data/train_456x256_000012/ego",
+    "data/train_456x256_000013/ego",
+    "data/train_640x480_000000/ego",
+    "data/train_640x480_000001/ego",
+    "data/train_640x480_000002/ego",
+    "data/train_640x480_000003/ego",
+    "data/train_640x480_000004/ego",
+    "data/val_1440x1080_000000/ego",
+    "data/val_1440x1080_000001/ego",
+    "data/val_1440x1080_000002/ego",
+    "data/val_1920x1080_000000/ego",
+    "data/val_1920x1440_000000/ego",
+    "data/val_1920x1440_000001/ego",
+    "data/val_2560x1440_000000/ego",
+    "data/val_2560x1920_000000/ego",
+    "data/val_342x256_000000/ego",
+    "data/val_3840x2160_000000/ego",
+    "data/val_456x256_000000/ego",
+    "data/val_456x256_000001/ego",
+    "data/val_640x480_000000/ego"
+  ],
+  "input_sources_sha256": "05555a3b6dd9ff20fee86ec0f602a888ff916d8ba13349a5e1e9596567e2db4b",
+  "sample_selection": {
+    "seed": 20260928,
+    "per_stratum": 4,
+    "pilot_strata": "official source split or split x camera"
+  },
+  "source_inventory": {
+    "sample_ids_sha256": "6c76fd5bb17731986afa7c933290cd4142c879634b6988c54ed86d911ef2277b"
+  },
+  "human_status": "pending",
+  "command": [
+    "outputs/EgoHOS/shared-v1.0/script/preprocess.py",
+    "build",
+    "--dataset",
+    "EgoHOS",
+    "--phase",
+    "full",
+    "--input",
+    "work/EgoHOS-full/extracted",
+    "--out",
+    "outputs/EgoHOS-full/shared-v1.0",
+    "--pilot",
+    "outputs/EgoHOS/shared-v1.0"
+  ],
+  "limitations": [
+    "OakInk MANO conversion not implemented until model-profile and translation parity verified",
+    "OakInk-Shape/OakBase excluded: object-space grasp/knowledge assets are not synchronized camera observations",
+    "No source visibility/contact guessed from geometry or masks"
+  ]
+}
diff --git a/EgoHOS/shared-v1.0/doc/pilot_human_review.json b/EgoHOS/shared-v1.0/doc/pilot_human_review.json
new file mode 100644
index 0000000000000000000000000000000000000000..eb02884b946700c0de8883c544e02774a1ba4676
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/pilot_human_review.json
@@ -0,0 +1,22 @@
+{
+  "reviewer": "用户（会话确认）",
+  "phase": "pilot",
+  "report_sha256": "2b8a2d2fa417f2d4ec440c598952d1ff47ee2b1a97aed11a9bc93d0d05ff23d4",
+  "recipe": {
+    "dataset": "EgoHOS",
+    "chunk": 128,
+    "seed": 20260928,
+    "annotations": false,
+    "split_scheme": "split0",
+    "code_sha256": {
+      "preprocess.py": "c5507d5ba4682f8a21ec77f4b9ff4af31fcf473eb98a95280e32b6ae9b9b5f20",
+      "media.py": "ace183f298d1073ee9d453f13464d58e0be685fe09a3ddd99a78c67b4c7337ec",
+      "validate.py": "3dc2bdadd89b03ca289673f40f45a724947972880dab6f537fb60354f90cc7b5",
+      "acquire.py": "1f010b87f6cc13d41058ba5b6b4ac89b4becc9ef4b5a15f887f00842dfdc6993",
+      "schema.json": "9a34b349ed9315fb0f6ccbe3425e852c971b5a4aaf26a094f4f4df15bfccc091",
+      "CONTRACT.md": "c34cdf6645f91233cc28d86f8abc82f019f78767d863e225f75722984d9adb19"
+    }
+  },
+  "approved": true,
+  "statement": "I inspected the listed HTML sample IDs and accept the documented scope and limitations."
+}
diff --git a/EgoHOS/shared-v1.0/doc/schema.json b/EgoHOS/shared-v1.0/doc/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..edb7cf7ce8e8e282d1fbfb5b39ef02a60321d2fb
--- /dev/null
+++ b/EgoHOS/shared-v1.0/doc/schema.json
@@ -0,0 +1,880 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "validity_revision": "usable-annotation-2026-09-20",
+  "annotation_validity": {
+    "meaning": "annotation usable for supervision, not merely record presence",
+    "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+    "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+    "policy_field": "components.<name>.valid_policy",
+    "visibility_is_independent": true,
+    "invalid_float_value": "NaN",
+    "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+    "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+  },
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_metadata": {
+    "required_fields": [
+      "view_type"
+    ],
+    "view_type_values": [
+      "ego",
+      "exo",
+      "unknown"
+    ],
+    "view_type_policy": "required per camera view; use ego or exo whenever the source data or capture setup establishes the viewpoint; use unknown only when no trustworthy source determines it; never omit or guess"
+  },
+  "keypoints": {
+    "mano21/v1": {
+      "count": 21,
+      "names": [
+        "wrist",
+        "thumb_1",
+        "thumb_2",
+        "thumb_3",
+        "thumb_4",
+        "index_1",
+        "index_2",
+        "index_3",
+        "index_4",
+        "middle_1",
+        "middle_2",
+        "middle_3",
+        "middle_4",
+        "ring_1",
+        "ring_2",
+        "ring_3",
+        "ring_4",
+        "little_1",
+        "little_2",
+        "little_3",
+        "little_4"
+      ],
+      "source_index_map": null,
+      "note": "adapter maps official MANO joint and fingertip convention"
+    },
+    "rhd21/v1": {
+      "count": 21,
+      "names": [
+        "wrist",
+        "thumb_1",
+        "thumb_2",
+        "thumb_3",
+        "thumb_4",
+        "index_1",
+        "index_2",
+        "index_3",
+        "index_4",
+        "middle_1",
+        "middle_2",
+        "middle_3",
+        "middle_4",
+        "ring_1",
+        "ring_2",
+        "ring_3",
+        "ring_4",
+        "little_1",
+        "little_2",
+        "little_3",
+        "little_4"
+      ],
+      "source_index_map_per_hand": [
+        0,
+        4,
+        3,
+        2,
+        1,
+        8,
+        7,
+        6,
+        5,
+        12,
+        11,
+        10,
+        9,
+        16,
+        15,
+        14,
+        13,
+        20,
+        19,
+        18,
+        17
+      ]
+    }
+  },
+  "camera": {
+    "opencv_fisheye4": {
+      "K": [
+        3,
+        3
+      ],
+      "distortion": [
+        "k1",
+        "k2",
+        "k3",
+        "k4"
+      ],
+      "projection": "theta=atan(r); theta_d=theta*(1+k1*theta^2+k2*theta^4+k3*theta^6+k4*theta^8); source ReInterHand"
+    },
+    "pinhole": {
+      "K": [
+        3,
+        3
+      ]
+    },
+    "fisheye624": {
+      "parameters": [
+        "f",
+        "cx",
+        "cy",
+        "k0",
+        "k1",
+        "k2",
+        "k3",
+        "k4",
+        "k5",
+        "p0",
+        "p1",
+        "s0",
+        "s1",
+        "s2",
+        "s3"
+      ]
+    },
+    "rational8": {
+      "K": [
+        3,
+        3
+      ],
+      "distortion": [
+        "k1",
+        "k2",
+        "p1",
+        "p2",
+        "k3",
+        "k4",
+        "k5",
+        "k6"
+      ]
+    }
+  },
+  "segmentation": {
+    "segment/uint16_labels/v1": {
+      "storage": "mask.mkv",
+      "dtype": "uint16",
+      "mapping": "instances.jsonl",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "reserved_codes": {
+        "0": "unassigned",
+        "65535": "ignore"
+      },
+      "region_codes": [
+        1,
+        65534
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    },
+    "segment/instance_polygons/v1": {
+      "storage": "polygons.jsonl",
+      "mapping": "instances.jsonl",
+      "frame_keys": [
+        "frame_index",
+        "instances"
+      ],
+      "instance_keys": [
+        "mask_value",
+        "polygons"
+      ],
+      "coordinate_unit": "pixel",
+      "overlap_allowed": true,
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref"
+      ],
+      "polygon_shape": [
+        "N",
+        2
+      ],
+      "holes_supported": false
+    },
+    "segment/foreground_intensity/v1": {
+      "storage": "mask.mkv",
+      "dtype": "uint16",
+      "value_range": [
+        0,
+        255
+      ],
+      "normalization_scale": 0.00392156862745098,
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+      "zero": "background",
+      "full_scale": "foreground",
+      "mapping": "foreground_category in component metadata",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "value_range",
+        "normalization_scale",
+        "foreground_category",
+        "entity_id",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    }
+  },
+  "note": "Registry for hand-warehouse/1.0. Read CONTRACT.md for field semantics. This is not a JSON Schema document or executable validator.",
+  "components": {
+    "mano/axis_angle61/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        2,
+        61
+      ],
+      "valid_shape": [
+        "T",
+        2
+      ],
+      "unit": "m",
+      "angle_unit": "rad",
+      "coordinate_frame": "camera",
+      "layout": {
+        "pose_axis_angle": [
+          0,
+          48
+        ],
+        "shape": [
+          48,
+          58
+        ],
+        "translation": [
+          58,
+          61
+        ]
+      }
+    },
+    "keypoints/uv/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        2,
+        21,
+        2
+      ],
+      "valid_shape": [
+        "T",
+        2,
+        21
+      ],
+      "unit": "pixel",
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "keypoints/xyz/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        2,
+        21,
+        3
+      ],
+      "valid_shape": [
+        "T",
+        2,
+        21
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "depth/z_float32/v1": {
+      "storage": "mkv",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "valid_shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "unit": "m",
+      "depth_type": "z",
+      "pixel_grid": "current_rgb",
+      "coordinate_frame": "current_rgb_camera",
+      "valid_storage": "mkv",
+      "allowed_storage_profiles": [
+        "mkv/depth_float32_bits/v1"
+      ],
+      "allowed_valid_storage_profiles": [
+        "mkv/bool_ffv1/v1"
+      ],
+      "required_metadata": [
+        "storage",
+        "valid_storage",
+        "depth_origin"
+      ],
+      "depth_origin_values": [
+        "measured",
+        "rendered",
+        "unknown"
+      ]
+    },
+    "transform/se3/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        4,
+        4
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "convention"
+      ]
+    },
+    "object/rigid/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        4,
+        4
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "convention"
+      ]
+    },
+    "camera/pinhole/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3,
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/rational8/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3,
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/opencv_fisheye4/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3,
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/fisheye624/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        15
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/distortion8/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        8
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/fisheye4_distortion/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        4
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "object/joint_state/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "J"
+      ],
+      "valid_shape": [
+        "T",
+        "J"
+      ],
+      "required_metadata": [
+        "joint_order"
+      ],
+      "unit_rule": "revolute radians; prismatic metres; mixed types declared per joint"
+    },
+    "object_keypoints/camera/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "K",
+        3
+      ],
+      "valid_shape": [
+        "T",
+        "K"
+      ],
+      "unit": "m",
+      "coordinate_frame": "camera",
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "object/dimensions/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "dimensions_semantics"
+      ],
+      "axis_order": [
+        "length",
+        "width",
+        "height"
+      ],
+      "scale_rule": "do not apply to mesh without source-supported size/scale relation"
+    },
+    "object_keypoints/template/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "K",
+        3
+      ],
+      "valid_shape": [
+        "K"
+      ],
+      "unit": "m",
+      "coordinate_frame": "object",
+      "static": true,
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "mesh/obj/v1": {
+      "storage": "obj",
+      "unit": "m",
+      "static": true,
+      "valid_shape": [],
+      "dependencies": "MTL and textures retained under owning object"
+    },
+    "rgb/uint8/v1": {
+      "storage": "mkv",
+      "dtype": "uint8",
+      "shape": [
+        "T",
+        "H",
+        "W",
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "color_order": "RGB",
+      "allowed_storage_profiles": [
+        "mkv/mjpeg_copy/v1",
+        "mkv/source_copy/v1",
+        "mkv/h264_rgb_lossless/v1",
+        "mkv/ffv1_rgb/v1"
+      ],
+      "required_metadata": [
+        "storage"
+      ]
+    },
+    "contact/relations/v1": {
+      "storage": "jsonl",
+      "valid_shape": [
+        "T"
+      ],
+      "frame_keys": [
+        "frame_index",
+        "relations"
+      ],
+      "absence_semantics": "no record is not a negative contact annotation"
+    },
+    "contact/vertex_region/v1": {
+      "storage": "pt",
+      "dtype": "bool",
+      "shape": [
+        "T",
+        "V"
+      ],
+      "valid_shape": [
+        "T",
+        "V"
+      ],
+      "required_metadata": [
+        "source_geometry_ref",
+        "target_geometry_ref",
+        "topology_id"
+      ],
+      "pilot_status": "defined_not_empirically_validated"
+    },
+    "contact/vertex_distance/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "V"
+      ],
+      "valid_shape": [
+        "T",
+        "V"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "source_geometry_ref",
+        "target_geometry_ref",
+        "topology_id",
+        "distance_definition"
+      ],
+      "pilot_status": "defined_not_empirically_validated"
+    },
+    "object/articulated/v1": {
+      "storage": "json",
+      "required_keys": [
+        "parts",
+        "joints"
+      ],
+      "part_keys": [
+        "part_id",
+        "parent_id",
+        "has_model",
+        "mesh_ref"
+      ],
+      "hierarchy_semantics": "declare mechanical articulation or semantic containment; containment alone does not imply joints",
+      "acyclic": true
+    },
+    "segment/uint16_labels/v1": {
+      "storage": "mkv",
+      "dtype": "uint16",
+      "mapping": "instances.jsonl",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "reserved_codes": {
+        "0": "unassigned",
+        "65535": "ignore"
+      },
+      "region_codes": [
+        1,
+        65534
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    },
+    "segment/instance_polygons/v1": {
+      "storage": "jsonl",
+      "mapping": "instances.jsonl",
+      "frame_keys": [
+        "frame_index",
+        "instances"
+      ],
+      "instance_keys": [
+        "mask_value",
+        "polygons"
+      ],
+      "coordinate_unit": "pixel",
+      "overlap_allowed": true,
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref"
+      ],
+      "polygon_shape": [
+        "N",
+        2
+      ],
+      "holes_supported": false
+    },
+    "segment/foreground_intensity/v1": {
+      "storage": "mkv",
+      "dtype": "uint16",
+      "value_range": [
+        0,
+        255
+      ],
+      "normalization_scale": 0.00392156862745098,
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+      "zero": "background",
+      "full_scale": "foreground",
+      "mapping": "foreground_category in component metadata",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "value_range",
+        "normalization_scale",
+        "foreground_category",
+        "entity_id",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    }
+  },
+  "storage_revision": "lossless-video-2026-09-18",
+  "sampling": {
+    "image": {
+      "single": {
+        "T": 1
+      },
+      "independent": {
+        "T": ">=2",
+        "required_frame_fields": [
+          "sample_id",
+          "source_locator"
+        ],
+        "temporal_relationship": false
+      },
+      "source_fps": null
+    },
+    "video": {
+      "sampling_types": [
+        "dense",
+        "sparse"
+      ]
+    }
+  },
+  "storage_profiles": {
+    "mkv/mjpeg_copy/v1": {
+      "container": "matroska",
+      "codec": "mjpeg",
+      "method": "stream_copy_from_source_jpeg",
+      "logical_dtype": "uint8",
+      "decoded_pixel_format": "rgb24",
+      "validation": [
+        "source_jpeg_packet_sha256_exact",
+        "all_packet_count_order_and_dimensions_exact",
+        "deterministic_decoded_rgb_sample_exact",
+        "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+      ],
+      "validation_policy": {
+        "id": "jpeg-packets-all-rgb-sample16/v1",
+        "sample_count_per_view": 16,
+        "short_views": "all_frames_if_T_less_than_16",
+        "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+        "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+        "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+        "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+        "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+      }
+    },
+    "mkv/source_copy/v1": {
+      "container": "matroska",
+      "codec": "source_codec_declared_in_metadata",
+      "method": "stream_copy_only",
+      "logical_dtype": "uint8",
+      "decoded_pixel_format": "rgb24",
+      "validation": [
+        "no_reencoding",
+        "all_decoded_rgb_frames_exact",
+        "presentation_frame_order_exact"
+      ]
+    },
+    "mkv/h264_rgb_lossless/v1": {
+      "container": "matroska",
+      "codec": "h264",
+      "encoder": "libx264rgb",
+      "crf": 0,
+      "decoded_pixel_format": "rgb24",
+      "logical_dtype": "uint8",
+      "gop": "declared_in_encoder_parameters",
+      "validation": [
+        "all_decoded_rgb_frames_exact"
+      ],
+      "forbidden": [
+        "lossy_rate_control",
+        "rgb_to_yuv420_subsampling"
+      ]
+    },
+    "mkv/ffv1_rgb/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "decoded_pixel_format": "rgb24",
+      "logical_dtype": "uint8",
+      "validation": [
+        "all_decoded_rgb_frames_exact"
+      ]
+    },
+    "mkv/depth_float32_bits/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "pixel_format": "gray16le",
+      "logical_dtype": "float32",
+      "logical_shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "encoded_shape": [
+        "T",
+        "H",
+        "2*W"
+      ],
+      "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+      "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+      "validation": [
+        "all_float32_bits_exact_including_nan"
+      ]
+    },
+    "mkv/bool_ffv1/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "pixel_format": "gray",
+      "logical_dtype": "bool",
+      "stored_values": [
+        0,
+        1
+      ],
+      "validation": [
+        "all_bool_values_exact"
+      ]
+    },
+    "mkv/uint16_ffv1/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "pixel_formats": [
+        "gray",
+        "gray16le"
+      ],
+      "logical_dtype": "uint16",
+      "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+      "validation": [
+        "all_integer_values_exact"
+      ]
+    }
+  },
+  "video_metadata": {
+    "required": [
+      "profile",
+      "codec",
+      "pixel_format",
+      "encoded_size",
+      "decoder",
+      "encoder_parameters",
+      "source_lossy"
+    ],
+    "source_lossy_values": [
+      true,
+      false,
+      "unknown"
+    ],
+    "timebase_role": "index_only",
+    "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+    "selection": "choose smaller complete candidate only after exact roundtrip validation"
+  }
+}
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diff --git a/EgoHOS/shared-v1.0/script/CONTRACT.md b/EgoHOS/shared-v1.0/script/CONTRACT.md
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+# 手物数据仓库统一约定 v1.0
+
+格式版本：`hand-warehouse/1.0`。存储约定：`lossless-video-2026-09-18`。
+
+范围限于手物交互重建。按本任务纳入的源模态转换，不要求每个数据集填满所有组件。缺失模态、尚未下载、语义未核实分别报告，不能互相替代。
+
+shared 层不是 raw 的完整镜像。处理者只需提取本次任务范围明确要求、语义已经核实且能够按本规范可靠转换的字段和模态。raw 中其余字段可以不纳入 shared，但必须在 `RAW_TO_SHARED.md` 中列出主要未纳入内容及原因。不得为了覆盖全部 raw 字段而猜测语义、虚构模态或保存未经定义的字段。未纳入 shared 不等于源数据不存在，原始信息仍以 raw 为准。
+
+本文定义各字段的含义与使用规则，机器可读注册表见 [schema.json](schema.json)。两者互相补充；如有冲突，应先核实并统一定义。
+
+## 1. 目录
+
+```text
+hand-datasets/
+  <dataset_name>/
+    raw/
+      doc/                         # 官方说明、来源与校验记录
+      script/                      # 官方下载脚本、必要包装脚本
+      data/                        # 保留原始包，不原地解压或改写
+    <dataset_version>/
+      doc/                         # 发布说明、schema、来源清单
+      script/                      # 转换、校验脚本及运行配置
+      data/
+        <sequence_id>/
+          <camera_id>/
+            metainfo.json
+            observation/
+              rgb.mkv
+              rgb_valid.pt
+              depth.mkv           # float32 深度的可逆视频编码
+              depth_valid.mkv     # 逐像素 bool 的无损视频编码
+            camera/
+              frames.jsonl
+              intrinsic.pt
+              intrinsic_valid.pt
+              extrinsic.pt
+              extrinsic_valid.pt
+            annotation/
+              mano/
+                motion.pt
+                motion_valid.pt
+                visibility.pt
+              keypoints/
+                uv.pt
+                uv_valid.pt
+                xyz.pt
+                xyz_valid.pt
+                visibility.pt    # 源数据有时提供
+              object/
+                <object_id>/
+                  assets/          # 该物体的网格、材质、纹理及部件资产
+                    <mesh_id>.obj
+                  pose.pt
+                  pose_valid.pt
+                  visibility.pt
+                  metainfo.json    # 物体身份、类别、mesh、尺寸与部件引用
+                  articulation.json
+                  keypoints/
+                  vertex_part_ids.pt
+                  joint_state.pt
+                  joint_state_valid.pt
+                  parts/<part_id>/pose.pt
+                  parts/<part_id>/pose_valid.pt
+              segment/
+                mask.mkv
+                mask_valid.pt
+                instances.jsonl
+              contact/
+                relations.jsonl
+                <subject_id>__<object_id>/  # 对应实体间的距离/接触区域
+```
+
+目录展示可能存在的组件，不要求填满。具体文件、字段和缺省规则分别见后续对应小节。原始包的解压发生在临时工作区，raw 内原始包保持不变。
+
+## 2. 相机视图的 metainfo.json
+
+### 2.1 顶层字段与发布信息
+
+每个相机视图必须有 `metainfo.json`。以下字段除另行说明外均为必填：
+
+| 字段 | 填写内容 |
+|---|---|
+| schema_version | 结构版本，本版为 `hand-warehouse/1.0` |
+| dataset_id | 数据集的逻辑标识 |
+| release_id | 本次数据发布版本，对应目录中的 dataset_version |
+| sequence_id | 所属序列或独立图片样本的标识 |
+| camera_id | 当前相机视图标识 |
+| sample_type | `image` 或 `video`，规则见 2.2 |
+| num_frames | 当前视图帧数 T，所有随帧组件按同一时间轴组织 |
+| hand_slots | 固定为 `["left", "right"]`；解剖学左右的使用规则见 5.1 |
+| sampling | 采样类型、源 FPS、帧索引引用及选择策略，见 2.2 |
+| camera | 当前相机模型与图像尺寸，见 2.3 |
+| components | 实际提供的组件及其文件、schema、有效性和来源，见 2.5 |
+| split | `train/val/test/unknown`，划分协议见 2.4 |
+| view_type | 必填，固定为 `ego/exo/unknown`。源数据或采集设置明确相机视角时必须填写 `ego` 或 `exo`；只有无法从可信来源确定时才填写 `unknown`，不得省略或主观猜测。多相机序列须逐视图分别填写。 |
+
+`dataset_version` 是数据发布版本；`schema_version` 是结构版本，两者独立。原始包校验摘要、官方来源和转换配置应记录在发布清单中。解压发生在临时工作区，原始包保持不变。
+
+### 2.2 图片、视频与 sampling
+
+`sample_type=image`：允许单图 T=1、`sampling.type=single`，也允许将独立图片批量装入一个视频容器，T>1 时 `sampling.type=independent`、`source_fps=null`。分片大小按数据集选择；FreiHAND 默认每片最多 4096 张，训练与评估分开，尾片不补帧。该数量不是其他数据集的强制要求；同批分辨率、相机模型类型及划分协议一致。每行 frames 必须有稳定且唯一的 `sample_id`、原始文件定位；内参、手形状等仍逐帧保留，不能因打包而共享错误的标定或身份。`sequence_id` 在这里仅是批次标识，不代表真实序列；不得按相邻帧推断跟踪、同步或接触延续。源 camera_id、image_variant、split 等随样本变化时放在 frames 的对应字段中。真实采集时间未知则 timestamp_ns=null。合成背景变体不视为真实多相机。
+
+`sample_type=video`：`sampling.type=dense/sparse`；`source_fps` 是真实采集 FPS，未知为 null。非连续取样必须保留源帧号和已知源时间，不可用采样后索引除以 FPS 冒充源时间。可另记采样策略与步长。
+
+shared 可以仅存所选 RGB 帧对应的数据。OakInk2 图像/动捕不同采样率时用 annotation_matches 明确映射；不要求将未采样动捕点硬塞进 T。多视角身份和源同步关系要保留；选择了哪些帧/相机及原因写在 sampling 中。
+
+`sampling.frames_ref` 指向 `camera/frames.jsonl`。帧号、逐帧时间、同步与标注匹配记录的结构见 4.1；MKV 封装时间的约定见 3.1。
+
+### 2.3 camera 模型声明
+
+`camera` 至少包含 `model`、`schema_id`、`image_size:[W,H]`。模型未知使用 `unknown` 和 `null`，不猜成针孔。实际内参、畸变及外参数组放在 `camera/`，各模型的固定结构见第 4 节。
+
+### 2.4 split 与划分协议
+
+保留上游划分，不将同一采集序列的相邻帧随意拆到不同集合。
+
+有官方划分时必填 split_scheme 和 split_source_locator，split_scheme 区分如 Dex-YCB s0/s1、ARCTIC P1/P2 等选定协议；不知道时 split=unknown 并给理由，不凭空分配。每发布可选择一种协议；存在逐帧差异时 frames 中的 split 覆盖视图默认值，并登记其来源。
+
+### 2.5 components 组件登记
+
+组件表的键是本发布内唯一名称，例如 `rgb`、`depth`、`mano`、`keypoints_uv`。每个已提供组件声明以下信息；模态特有参数在第 3–5 节对应小节中定义。
+
+| 字段 | 约定 |
+|---|---|
+| available | 本发布是否提供该组件；缺省也表示未提供 |
+| schema_id | 已登记的具体存储与语义类型，不得临时创造未登记名称 |
+| data_ref | 数据文件路径，相对当前相机视图根目录，使用 POSIX 路径 |
+| axes | 数组各维度的含义 |
+| valid_ref / valid_axes | 有效性文件及其维度；语义由所属组件规定 |
+| storage / valid_storage | 视频数据／视频有效性的存储配置；包含已登记 profile、实际 codec、pixel_format、encoded_size，见 3.1；非视频组件按原数组规则 |
+| provenance | source / format_conversion / derived，以及转换或生成方法 |
+| source_locator | 原文件、压缩包成员和记录／帧的定位信息 |
+
+不存在的组件允许缺省或写作 `{ "available": false, "reason": "..." }`，不用引用不存在的文件。静态网格可声明标量有效性；具体见 5.4。观测的 valid 见第 3 节，标注的 valid 与 visibility 区分见 5.1。
+
+`provenance.kind` 使用 source/format_conversion/derived，`method` 说明转换或生成方法；发布清单补充脚本版本、参数、输入摘要。`source_locator` 定位到原始文件/压缩包成员和记录、帧号；允许引用有完整溯源清单的前一发布，但链条最终应能回到 raw。不得仅写一个无法定位记录的数据集名字。
+
+`.pt` 仅保存 CPU Tensor，不保存 Python 自定义对象；浮点默认 float32，有效性为 bool，浮点无效位置填 NaN。所有随帧数组保留首维 T，单帧也不 squeeze；静态资产按其 schema 单独声明。单位、坐标及固定形状见各组件小节。
+
+机器注册表 `schema.json` 中，`components` 固定解码后的数据类型、形状、单位和语义；`storage_profiles` 固定视频内的编码／还原方式。不得临时创造未登记的 schema_id 或 storage.profile。同一语义允许选择其登记的不同无损存储 profile。
+
+## 3. observation 观测数据
+
+### 3.1 rgb.mkv 与 rgb_valid.pt
+
+RGB、深度和栅格 mask 在 shared 中统一使用 MKV 视频容器，不展开为逐帧图片或图像 tar。MANO、关键点、位姿等数值标注仍用数组；分割的重叠多边形仍用 JSONL，不为了视频化而有损栅格化。单图可为一帧视频，独立图批量封装规则见 2.2。
+
+**无损指共享层不增加信息损失。** 源 JPEG／H.264 等本身可能已是有损编码，原样保留并不恢复已丢失信息；不得重新以有损 JPEG、H.264、H.265、AV1 参数编码。源视频／图片的色彩、位深、分辨率和几何关系不得被静默改变。RGB schema `rgb/uint8/v1` 的统一读取结果是 `[T,H,W,3] uint8`、RGB 通道，有效性仍为 `rgb_valid.pt [T] bool`。超出该 schema 的高位深或 HDR 源必须报告并扩展定义，不能降到 8 位冒充无损。
+
+#### 3.1.1 允许的 RGB 存储 profile
+
+| storage.profile | 编码方式 | 适用规则 |
+|---|---|---|
+| mkv/mjpeg_copy/v1 | 源 JPEG 压缩帧直接封装成 MJPEG/MKV | 不解码重编码；保持每张 JPEG 码流字节；普通 8 位 JPEG 且 MKV/解码器支持时使用 |
+| mkv/source_copy/v1 | 已有视频的 RGB 对应码流直接封装为 MKV | 仅源编码能被 MKV 和选定解码器支持时使用，不伪造帧；需维持正确的呈现顺序与依赖帧 |
+| mkv/h264_rgb_lossless/v1 | libx264rgb 的真正无损模式 | 必须 RGB 路径、CRF=0，并通过全帧 RGB 字节比较；不能替换成默认 libx264 YUV420 路径 |
+| mkv/ffv1_rgb/v1 | FFV1 的 RGB 可逆编码路径 | 作为通用候选；解码回 RGB 后必须逐字节相同，不做色度降采样 |
+
+优先尝试源码流直接封装；不能封装或需要选择稀疏帧时，比较 FFV1 与无损 RGB H.264。对完整待发布片段，只在通过无损验收的候选中选择文件较小者，不假设某个编码器总最小；编码时间和随机读取代价记入构建记录。中间候选不作为 shared 多份常驻。H.264 跨帧压缩允许较长 GOP，必须记录 GOP／关键帧间隔，随机取帧需从前一关键帧正确解码，不能把 seek 返回的近似帧当目标帧。
+
+普通源视频可以保留它已有的有损码流，但不允许新增有损重编码。源是 VRS 等记录容器时，先查明图像流实际编码：已有兼容 JPEG／视频包可直接提取封装，否则解码后使用真正无损编码；不把整个 VRS 改后缀当 MKV。PNG 不要求直接塞进 MKV，可选上表两种无损重编码方式。
+
+#### 3.1.2 视频配置与校验
+
+每个视频组件在 metainfo 中登记 `storage`，至少包含：
+
+| 字段 | 内容 |
+|---|---|
+| profile | 上述登记的存储 profile；语义仍由组件 schema_id 指定 |
+| codec | 文件中实际 codec，例如 mjpeg、h264、ffv1；source_copy 必须写实际 codec |
+| pixel_format | 实际编码像素格式；不得仅凭编码器参数猜测 |
+| encoded_size | 视频物理像素尺寸 `[width,height]`；深度可能不同于逻辑尺寸，见 3.2 |
+| decoder | 解码器名称、版本，以及颜色矩阵／范围／旋转处理等实际像素解释配置；未知的源色彩声明 unknown，不擅自称为 sRGB |
+| encoder_parameters | 重编码时的编码器、版本、无损模式和 GOP 等参数；直接封装时记录 stream_copy |
+| source_lossy | 源是否已有损：true / false / unknown；不是共享层再有损编码的许可 |
+
+无损重新编码的 RGB 必须使用同一固定解码配置，比较**全部帧**的统一 RGB 字节、数量及顺序。MJPEG-copy 不重新编码，生产校验采用 `jpeg-packets-all-rgb-sample16/v1`：从 MKV 逐包取回，对全部源 JPEG 的 SHA256、数量、顺序和图像尺寸进行精确核对；每视图另用同一固定解码配置比较最多 16 帧的源与目标 RGB 字节（含首尾与四分位位置，其余由固定种子抽取）。记录策略、实际索引、种子与结果；不足 16 帧时全查。试点与最终完整序列随机抽查，对选中视图的全部帧复核解码 RGB。历史已做全帧像素检查的证明继续保留。
+
+逐包一致与像素抽查的范围必须分别声明，不能将抽查写成全量像素检查。不能用 PSNR 高、视觉相似、编码器参数称为 lossless 等替代精确检查。同时保留源编码的色彩标记和必要元数据；原图有 EXIF/色彩/方向等元数据时保留 raw 和必要来源信息；不能静默自动旋转图像而不更新像素对应与标定。以上加速策略仅适用于原 JPEG 码流直接封装，不适用于深度、标签或重新编码的 RGB。
+
+所有共享视频的呈现帧顺序与统一 frame_index 一一对应，每个栅格组件恰好 T 帧。MKV 的 `storage_fps` 只服务封装，声明 `timebase_role=index_only`；source_copy 可以保留源 PTS 以避免改坏码流，但真实时间与标注对齐仍以 frames 为准。未知真实时间保留 null。缺失某帧组件时保留索引位置并使用该组件的无效占位／valid 标识，不删帧；不能以重复相邻图像冒充已知观测。
+
+### 3.2 depth.mkv 与 depth_valid.mkv
+
+深度语义仍是 `depth/z_float32/v1`：统一读取结果 `[T,H,W] float32`，米制相机 Z 分量，`depth_type=z`。有效深度有限且大于 0，无效位置为 NaN。`depth_origin=measured/rendered/unknown` 必填，来源不明时 unknown。默认与 RGB 共用像素网格及相机参考系，不处理未注册 RGB-D。射线距离必须依据源定义转换成 z，不能只改声明。
+
+#### 3.2.1 float32 的可逆视频编码
+
+采用 `mkv/depth_float32_bits/v1`：**不是把深度量化成 uint16**，而是将规范化后每个 IEEE-754 float32 的完整 32 位拆成两个 uint16，使用 FFV1 gray16le 保存。视频物理尺寸为 `[2W,H]`：每行前 W 列存原 float32 位串的低 16 位，后 W 列存高 16 位。读取时先合并为 uint32 位串，再按 float32 重新解释，不能数值 cast 成 float32。
+
+```text
+u = reinterpret_uint32(depth_float32)
+encoded[:, :, :W] = uint16(u & 65535)
+encoded[:, :, W:] = uint16(u >> 16)
+u_restored = uint32(low) | (uint32(high) << 16)
+depth_float32 = reinterpret_float32(u_restored)
+```
+
+统一采用 little-endian 原始字节接口。`camera.image_size` 始终是逻辑 RGB 尺寸 `[W,H]`，不能改成编码视频的 2W；`storage.encoded_size=[2W,H]`。包括 NaN 在内的 float32 位串必须全帧完全恢复，不允许改成 float16、整数毫米、归一化灰度或 RGB 伪彩色。这里的无损验收基准是完成既定米制／坐标转换后的标准 float32 数组，不声称消除 float64→float32 等原约定转换本身的舍入。
+
+#### 3.2.2 逐像素有效性
+
+`depth_valid.mkv` 使用 `mkv/bool_ffv1/v1`，FFV1 gray8（FFmpeg pixel_format=`gray`），物理尺寸 `[W,H]`。像素只能为 0 或 1，统一解码为 `[T,H,W] bool`，逐像素含义不变。这样不再把庞大的 bool 数组保存为未压缩 `.pt`；标注的帧级／手级 valid 仍保留原有小数组。
+
+登记示例（W、H 以下取 640、480；decoder/encoder_parameters 的实际版本由转换器填写）：
+
+```json
+{
+  "available": true,
+  "schema_id": "depth/z_float32/v1",
+  "data_ref": "observation/depth.mkv",
+  "axes": ["T", "height", "width"],
+  "valid_ref": "observation/depth_valid.mkv",
+  "valid_axes": ["T", "height", "width"],
+  "unit": "m",
+  "depth_type": "z",
+  "depth_origin": "measured",
+  "invalid_value": "NaN",
+  "storage": {
+    "profile": "mkv/depth_float32_bits/v1",
+    "codec": "ffv1",
+    "pixel_format": "gray16le",
+    "encoded_size": [1280, 480],
+    "source_lossy": false,
+    "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+    "encoder_parameters": {"name": "ffv1", "level": 3, "version": "填写实际版本"}
+  },
+  "valid_storage": {
+    "profile": "mkv/bool_ffv1/v1",
+    "codec": "ffv1",
+    "pixel_format": "gray",
+    "encoded_size": [640, 480],
+    "source_lossy": false,
+    "decoder": {"name": "ffmpeg", "version": "填写实际版本"},
+    "encoder_parameters": {"name": "ffv1", "version": "填写实际版本"}
+  },
+  "provenance": {"kind": "format_conversion", "method": "源深度转米制 z float32；逐位打包为 FFV1"},
+  "source_locator": {"file": "raw/data/depth.zip", "member": "depth/0001.png"}
+}
+```
+
+深度和有效性视频都必须逐帧解码验收：float32 位串相同、bool 相同、T 相同，并检查 valid=true 的正有限值和 valid=false 的 NaN。读取器必须按 profile 还原，不能把 depth.mkv 的编码像素直接当深度值。
+
+## 4. camera 相机、帧索引与标定
+
+### 4.1 frames.jsonl：索引、时间与对应关系
+
+`camera/frames.jsonl` 每行对应统一帧号 `frame_index=0..T-1`，是所有组件的唯一行索引。至少包含 `frame_index`、`timestamp_ns`、`clock_domain`、`source_locator`；原始帧号/文件名保存在 `source_frame_id`，不能被新帧号替代。每行还可有 `annotation_matches`，按组件键保存 `source_frame_id`、`source_timestamp_ns`、`clock_domain`、`match_method` 和 `delta_ns`；方法为 exact/index_map/nearest/interpolated，索引映射无时间依据时 delta_ns=null。nearest 必须记录允许误差；interpolated 必须记录两端源标识和权重。delta_ns 仅在同一时间域中计算为源标注时间减图像时间。多相机共享 `sequence_id`；已知同步关系必须用 `sync_id` 或明确的源帧/时间映射表达，不能仅凭相同数组索引认定同步。
+
+每个视图必须提供此文件。源时间使用整数纳秒及 `clock_domain`，未知写 JSON `null`；不同时间域不能直接比较。逐帧 split 可以覆盖 metainfo 的视图默认划分，并保留来源，见 2.4。
+
+### 4.2 坐标系与标定通则
+
+| 项目 | 统一约定 |
+|---|---|
+| 相机坐标 | 右手系，X 向右、Y 向下、Z 向前，长度单位米 |
+| 齐次变换 | 列向量，`p_target = T_target_from_source @ p_source` |
+| 图像坐标 | `(u,v)`；左上像素中心为 `(0,0)`，u 向右、v 向下，单位 pixel |
+| 固定标定 | 本版展开为 T 份，同一 schema 不混用静态与动态形状 |
+| 分辨率或模型变化 | 分开存储为视图／片段，不在同一组件中混装 |
+| 未知标定 | 相机内外参允许缺省，不填单位阵冒充已知标定 |
+
+### 4.3 intrinsic.pt 与畸变参数
+
+| model / schema_id | intrinsic.pt 的固定结构 |
+|---|---|
+| pinhole / camera/pinhole/v1 | `[T,3,3]`，标准 K，最后一行为 `[0,0,1]`；限无畸变或已校正图像 |
+| fisheye624 / camera/fisheye624/v1 | `[T,15]`，`[f,cx,cy,k0,k1,k2,k3,k4,k5,p0,p1,s0,s1,s2,s3]`，采用 Project Aria Fisheye624 投影定义 |
+| opencv_fisheye4 / camera/opencv_fisheye4/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件 `distortion_ref` 指向 `[T,4]`，顺序 `[k1,k2,k3,k4]`，使用 OpenCV fisheye 等距角度多项式；已由 ReInterHand 实际原始相机参数验证 |
+| rational8 / camera/rational8/v1 | `intrinsic.pt [T,3,3]` 保存 K；同组件必需 `distortion_ref` 指向 `[T,8]`，顺序 `[k1,k2,p1,p2,k3,k4,k5,k6]` |
+| unknown / null | 不提供可用 intrinsic，不声称可以投影 |
+
+有畸变的其他模型必须注册新 schema，固定参数名、顺序和投影公式；不能塞进针孔 K 后遗失畸变项。`metainfo.camera.schema_id` 必须与 intrinsic 组件一致。同一模型的别名不可隐式改变参数布局。
+
+`opencv_fisheye4`：对前方点令 `x=X/Z, y=Y/Z, r=sqrt(x²+y²), theta=atan(r)`，`theta_d=theta*(1+k1*theta²+k2*theta⁴+k3*theta⁶+k4*theta⁸)`；`u=fx*x*theta_d/r+cx, v=fy*y*theta_d/r+cy`。光轴 r=0 时使用连续极限 `theta_d/r=1`。四系数不能按普通径向畸变解释，也不能补零冒充 Fisheye624。
+
+rational8 对归一化坐标 x=X/Z、y=Y/Z，令 r²=x²+y²，径向项为 `(1+k1*r²+k2*r⁴+k3*r⁶)/(1+k4*r²+k5*r⁴+k6*r⁶)`；再加 p1/p2 切向项并经 K 得到像素。`distortion_ref` 与 intrinsic 共用 `[T]` valid，保存到 camera/distortion.pt。不得把这种畸变三维坐标当作真实相机系 xyz 保存。
+
+内参有效性为 `intrinsic_valid.pt [T] bool`；有畸变时使用对应 `distortion_ref`。同一模型的参数名和顺序固定。
+
+### 4.4 extrinsic.pt 与 extrinsic_valid.pt
+
+`extrinsic.pt` 为 `[T,4,4]`，schema `transform/se3/v1`，方向固定 `T_camera_from_world`，有效性 `[T]`。世界坐标含义及源坐标转换记录在 provenance 中；只有相机坐标标注不要求虚构世界坐标。旋转块须正交且 determinant≈1。
+
+## 5. annotation 标注数据
+
+### 5.1 标注通则：手槽、valid 与 visibility
+
+手槽固定为 0=left、1=right，指解剖学左右，不按画面位置或可见性重排。本阶段只处理左右手两槽，不处理更多手。实体引用使用 `hand_left`、`hand_right`；手槽不证明跨采集片段的个人身份。
+
+#### 5.1.1 valid：标注是否可用于监督
+
+标注的 `valid=true` 表示：有对应实际标注、通过本 schema 数值检查，且通过来源明确提供并适用于该组件的有效性/QA 条件。`valid=false` 可由缺标注、无法建立帧对应、源无效占位或官方 QA 不通过导致；不等于画面没有手或手不可见。各组件独立判断，MANO 按 `[T,2]` 标记左右手，关键点按 `[T,2,K]` 标记。
+
+**2026-09-20 将标注 valid 从“仅表示存在”修订为“可用于监督”，字段形状不变。** 公式为 `source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass`。没有官方 QA 时省略最后一项，不伪造 QA；不能把未知当成已通过已存在的 QA。适配器在组件 `valid_policy` 中记录具体规则、来源、聚合/广播作用域。缺失官方必需 QA 的记录须明确处理为未知或暂停验收，不能静默通过。无效浮点项仍填 NaN，原始数据保留在 raw；被排除原值可用于 doc 中明确标识的诊断渲染，不能冒充 shared 有效监督。
+
+本地筛选须依据明确的规则，并在 `valid_policy.local_excluded_frame_indices`、排除原因和正式报告中记录。单个可疑投影作为问题记录，不直接硬编码排除该帧。已确认的曝光异常须明确作用范围，不能把一个序列的“前两帧有问题”套到所有序列。RGB 编码完好与几何监督可用是两件事，允许保留原图及帧索引而屏蔽对应标注；未发现问题不等于完成全部人工审核，试点待确认时不能作为已验收发布使用。
+
+源字段必须按官方语义映射，不能直接按名字复制：
+
+| 来源 | 官方含义 | 映射原则 |
+|---|---|---|
+| [H2O](https://github.com/taeinkwon/h2odataset) 的手标注首位 | 0 未标注、1 已标注 | 可作为对应手标注存在性的依据，不推断未标注原因 |
+| [HOT3D](https://github.com/facebookresearch/hot3d#using-mask-files) 的 pose_available、visible、mask_qa_pass | 分别表达姿态存在、可见性、对画面中可见手物位姿的人工 QA | 逐手/物体记录决定存在性，再 AND 当前相机当前帧 QA；QA 是整帧聚合判定，保守排除该帧全部手物几何监督，不声称每个实体均有错误；源可见性基于位姿推导时，其自身 valid 同样受 QA 约束 |
+| [Dex-YCB](https://github.com/NVlabs/dex-ycb-toolkit#loading-dataset-and-visualizing-samples) 的全零 pose_m、全 -1 joints | 不可见的手或缺标注共用占位表示 | 对应组件 valid=false；不能从该值判定 visibility=false，也不能声称原因是动捕缺失 |
+| [ARCTIC](https://github.com/zc-alexfan/arctic/blob/master/docs/data/data_doc.md) 的 is_valid/left_valid/right_valid | 是否有效用于官方评估 | 核实每项筛选依据；不把可见性/评估选择直接等同 QA。本次原始 mano.npy 不含这些派生评估标记，按原记录与数值检查生成 valid，并声明没有额外官方 QA 输入 |
+
+源 valid=false 若代表评估筛选或其他条件，应检查实际标注和源约定后映射；不能一律清空坐标。反之，占位零或 -1 只有在源官方明确规定时才判为缺失，不泛化成所有数据的判断规则。
+
+`valid=true` 的标注应通过其 schema 的数值与形状检查；检查失败必须报告或修复，不能静默隐藏。QA 不通过应报告排除数量与原因，并保留可追溯信息；不能把这类无效称为缺标注。RGB、深度等观测有效性仍按各自 schema；手物 QA 不自动否定 RGB、静态相机内参或物体资产。相机外参是否过滤须依据其自身来源规则；HOT3D 本次保留源外参用于溯源和诊断，不能用其存在性证明 QA 失败帧的几何监督正确。
+
+#### 5.1.2 visibility：可见性属性
+
+附加质量属性只保留 `visibility`，存放在对应模态或标注集内，不设 quality 目录。同一份可见性仅存一次；uv 与 xyz 共用时引用同一个文件。
+
+visibility 通过所属组件 attributes 登记 data_ref、axes、dtype、visibility_type、meaning、provenance、source_locator；未知值通过属性 valid_ref 标识，全已知可声明 valid=true。比例与布尔值不相互强转，也不按任意阈值二值化。没有 MANO 时，整手可见性挂在 keypoints 组件，使用独立的 whole_hand_visibility.pt，明确 meaning=whole_hand，不与逐关键点可见性混用。
+
+`valid` 回答“这项标注能否用于监督”，`visibility` 回答“在当前图像中是否可见（或可见多少）”。不可见的手或关键点仍可有有效标注；不能因 visibility=0 就清空坐标。源没有 visibility 时缺省或标为未知，不由 valid 推断。
+
+| 情况 | 手标注 valid | visibility |
+|---|---|---|
+| RGB 已开始录制，动捕尚未开始，没有其他对应手标注 | false | 以独立源可见性标注为准；没有则未知 |
+| 手被遮挡，但源有标注且通过适用有效性检查 | true | 若源声明不可见，则 false 或源可见比例 |
+| 手可见且有对应标注，通过适用 QA | true | 若源有此信息，则 true 或源可见比例 |
+| 手可见，但该帧漏标或动捕中断 | false | 可以为 true，但必须有独立来源依据 |
+| 有手部参数，但官方 QA 明确不通过 | false | 不据此断言不可见；源可见比例若基于不可靠位姿，其属性 valid 也为 false |
+
+visibility 自己的 valid_ref 表示该可见性标注是否可用，不复用 MANO 或关键点坐标的 valid_ref；明确记录源存在性及适用的 QA 条件。上述例子不要求凭图像自动生成可见性。
+
+不设 quality 组件或目录，不另存拟合误差、曝光、截断比例等通用质量数组。适用的官方 QA 合并进对应 valid，在 `valid_policy` 记录来源与规则；正式校验报告保留原始 QA 时间区间及排除统计。原始 QA 文件仍在 raw，官方 split 保留。
+
+### 5.2 mano：参数与整手可见性
+
+MANO schema `mano/axis_angle61/v1`：`motion.pt [T,2,61]`，顺序为完整 16 关节 axis-angle 48、shape betas 10、全局平移 3。姿态使用弧度，平移米，位于当前相机系。有效性 `[T,2]`；缺少一侧整槽 NaN。PCA 必须先按对应模型展开，不能将 PCA 系数直接填入 axis-angle。
+
+`model_profile` 必须固定 MANO 版本、`flat_hand_mean=true`、左手 shapedirs 修正状态。平移表示模型输出顶点所加的全局平移，不是某个腕关节的坐标；源语义不同须转换。shape 无量纲。模型权重不作为共享数据附带上传。
+
+`mano/visibility.pt [T,2]` 保存源整手可见比例时使用 float32 [0,1]，`visibility_type=ratio`；只有实际采用 0–100 的百分比才除以100，不能只凭表头判断。HOT3D 的 `visibility_ratio[%]` 实值已经是0–1，官方读取器原样读取，必须保留原值。通过 MANO 组件 attributes 登记，使用独立有效性，不复用 motion_valid。
+
+手部共享表示仅保留 MANO 参数与选定关键点，不发布独立手表面几何。需要渲染或接触计算时由 MANO 参数生成，几何引用使用固定 MANO 模型配置与顶点顺序；不影响 object 下的物体模型和材质资产。
+
+### 5.3 keypoints：二维点、三维点与逐点可见性
+
+关键点独立于 MANO 参数存在；没有 motion 也可有关键点。`uv.pt [T,2,K,2]` 与 `xyz.pt [T,2,K,3]`，有效性 `[T,2,K]`，分别采用 `keypoints/uv/v1` 和 `keypoints/xyz/v1`。K 由 `keypoint_schema` 固定，不能在同一数组中逐帧变化；同体系统一顺序。数值使用像素坐标或米制相机系。
+
+| keypoint_schema | K 与固定索引 |
+|---|---|
+| mano21/v1 | 21；wrist 后依次 thumb/index/middle/ring/little，每指由近端至指尖 |
+| rhd21/v1 | 21；保持 wrist，然后五指由近端至指尖；原始每指 tip→palm 反序。只统一顺序，不宣称解剖学位置等价于 MANO |
+
+具体名称与源索引映射见 `schema.json`。当前只登记 MANO21 与 RHD21；不登记 MediaPipe、ReInterHand capture21 或 fit24 中间关键点。
+
+每个发布中的每个视图只提供一套标准手关键点，直接放在 keypoints 下，不设 annotation_set_id 字段或目录。uv 与 xyz 是这套关键点的二维与三维表示，不是两套标注。provenance 分别记录二者来自原始标注还是计算结果；不要求已有 2D 必须由当前 xyz 重新投影覆盖。
+
+ReInterHand 的 Original keypoints、Original fits keypoints 及带前臂关键点不纳入 shared；如需手关键点，从选定 MANO 参数生成 MANO21 并记录模型/指尖回归约定。其他数据集默认使用选定的官方关键点，不额外生成平行的 MANO 派生集合。后续主动更换生成方法或结果时发布新的 dataset_version，不在同一版本内增设方法分支。
+
+manual/projection/fitting 只记录在 provenance，不作为目录或类型分支。不得通过坐标为零、是否出画或 visibility 判断标注是否存在。派生标注仅在实际生成并通过数值检查后声明 valid=true；没有生成的项为 false，不伪造坐标。
+
+`keypoints/visibility.pt [T,2,K]` 保存源逐点可见性，布尔表示使用 `visibility_type=boolean`。uv 与 xyz 共用时引用同一份文件。整手比例属性没有 MANO 可挂载时使用 `whole_hand_visibility.pt [T,2]`，`meaning=whole_hand`；它与逐点可见性分开，通用登记规则见 5.1。
+
+不使用 annotation_set_id；ReInterHand 中间关键点集合不进入 shared，raw 保留来源文件。
+
+### 5.4 object：身份、资产、位姿与几何
+
+#### 5.4.1 metainfo.json：物体身份与跟踪作用域
+
+手用 `hand_left`、`hand_right` 及槽位 0、1 表示，不再另存 entities.json。物体身份登记在 `annotation/object/<object_id>/metainfo.json`，必填 `object_id`、`identity_scope`、`scope_id`、`source_locator`；可含 `category_id`、`source_object_id`、`mesh_ref`、`has_model`、部件引用。即使只有分割、没有 pose/mesh，也可只有这份物体元信息。
+
+`identity_scope` 为 frame/subsequence/sequence，`scope_id` 指定具体作用域；跨相机共享身份只在已有源对应证据时使用。VISOR 按官方 subsequence 与实体名关联时记录 `association_basis=official_subsequence_entity_name`，不能声称这是全视频物理实例跟踪。无需与 object_id 一一对应的标准 track_id；官方独立轨迹标识存在时保留为 source_track_id，并声明作用域。手槽不证明跨采集片段的个人身份。
+
+#### 5.4.2 assets：网格、材质与纹理
+
+资产属于物体，统一存放在 `annotation/object/<object_id>/` 内，可用 `assets/` 子目录组织网格、材质、纹理和部件资产。序列层不设共享 assets。所有引用采用视图根目录为基准；其中物体资产组件的 `data_ref` 与部件的 `mesh_ref` 采用相对于当前视图目录的 POSIX 路径，例如 `annotation/object/cup_01/assets/cup.obj`，且必须指向所属物体目录内的资产。OBJ 内部的材质、纹理引用遵循文件格式自身的相对路径规则，依赖文件一并保留。不同相机视图允许各自保存物体资产，不要求跨视图去重；下载物体时包含其资产依赖。
+
+网格 schema 为 `mesh/obj/v1`，顶点米制，静态有效性可直接声明标量 `valid`，或通过 `valid_ref` 指向标量 bool Tensor；不按 T 重复静态资产。由物体／部件通过引用关联，不仅凭文件名猜测。
+
+#### 5.4.3 pose.pt、pose_valid.pt 与 visibility.pt
+
+刚体 schema 为 `object/rigid/v1`，`pose.pt [T,4,4]` 固定为 `T_camera_from_object`，平移米制，有效性 `[T]`。物体坐标原点、轴向必须与 mesh 一致；旋转块为正交矩阵且 determinant≈1。
+
+`object/<id>/visibility.pt [T]` 保存源物体可见比例，float32 [0,1]，`visibility_type=ratio`。通过所属物体组件 attributes 登记，独立表达其有效性。
+
+#### 5.4.4 articulation.json、部件位姿与 joint_state.pt
+
+关节物体：schema `object/articulated/v1`，`articulation.json` 登记 `parts` 与 `joints`。每个 part 有 `part_id`、`parent_id`（根为 null）、`has_model`、`mesh_ref`；has_model=false 时 mesh_ref=null，结构节点允许没有网格。父链不得成环。每个 joint 有唯一 `joint_id`、`parent_part`、`child_part`、`type`；fixed 单位 null、revolute 弧度、prismatic 米。有源限位时保存 `limits:[lower,upper]`；没有时 null，不伪造上下限。部件姿态统一为 `T_camera_from_part [T,4,4]`，不与父局部变换混装。
+
+若提供 `joint_state.pt [T,J]`，必须登记固定 joint_order、轴在父部件系的表达、零位变换与正方向，valid 为 `[T,J]`；缺少这些运动学定义时只发布部件位姿与已有来源信息，不伪造可驱动的关节模型。关节值与部件位姿同时存在时需验证一致性。
+
+部件层级应声明语义：纯语义包含树允许 `joints=[]`，不将包含关系推断为机械关节。关节状态 schema 为 `object/joint_state/v1`；转动用弧度、移动用米，混合类型按 joint_order 对应的关节定义解释。
+
+#### 5.4.5 dimensions、几何关键点与顶点部件标签
+
+`dimensions.pt [T,3]` 采用 `object/dimensions/v1`，有效性为 `[T]`。
+
+物体/部件 `dimensions` 为 `[length,width,height]` 米，轴顺序与该 mesh 的局部坐标关联，登记 `dimensions_semantics` 和来源。HOI4D dimensions 与 mesh 的具体尺度关系未验证前标为 source_unspecified，保留源值并暂停依赖该关系的几何验收，不能默认为待施加缩放。需要施加缩放时明确 scale_applied_to_mesh，禁止重复应用。
+
+源物体关键点放 `object/<id>/keypoints/xyz.pt [T,K,3]`（相机系）或 `template_xyz.pt [K,3]`（物体系），schema 分别为 object_keypoints/camera/v1 和 object_keypoints/template/v1。明确 keypoint_schema、keypoint_names、单位及有效性；模板点配合 pose 可投影，无须重复生成全部帧。H2O 中心/角点/棱中点，以及 ARCTIC 部件点分别登记具体源顺序，不套用手的 21 点 schema。每个发布选择一套源几何点并记录选择，不另建方法分支。
+
+`vertex_part_ids.pt [V] int64` 绑定 `mesh_ref` 与 topology_id，登记整数到 part_id 的映射；未知用 -1。ARCTIC 不同拓扑的资产分别绑定，不能共用未经转换的顶点标签。部件层级中 is_instance/has_model 等源属性保留，几何文件仍全部归 object。
+
+### 5.5 segment：分割类别、编码与覆盖范围
+
+每个发布选择一套任务使用的分割表示，不建立方法／标注集分支。没有分割就不创建 segment 目录，不为其他模态创建空的实体表。
+
+| schema_id | 文件与数值语义 |
+|---|---|
+| segment/uint16_labels/v1 | mask.mkv；整数区域编号，通过 instances.jsonl 解释 |
+| segment/instance_polygons/v1 | polygons.jsonl；实例多边形，可重叠；当前不支持孔洞拓扑 |
+| segment/foreground_intensity/v1 | mask.mkv；0–255 前景强度，不是区域编号或经过校准的概率 |
+
+#### 5.5.1 uint16 标签图与 instances.jsonl
+
+组件声明 `segmentation_type=semantic/instance/part`；同一组件固定一种类型。`mask.mkv` 解码为 `[T,H,W] uint16`，schema `segment/uint16_labels/v1`，使用 `mkv/uint16_ffv1/v1`，FFV1 gray16le 无损；只有所有区域编码均 ≤255 时允许 gray8（pixel_format=gray），读取后转 uint16，数值不变。禁止调色板和有损编码。有效性 `[T]`，表示该帧有可用分割标注。数值 0 固定为 unassigned，65535 固定为 ignore，1–65534 为区域编码，不能直接等同 entity_id。
+
+`instances.jsonl` 每帧一行，包含 `frame_index` 与 `labels`。每条标签包含 `mask_value`、`category_id`、`entity_id`、`part_id`；无意义或未知字段为 null。semantic 允许 entity_id=null；instance 必须引用 hand_left/hand_right 或登记的 object_id；part 还须关联登记部件。源实例 ID 和其他属性分别保存在 `source_instance_id`、`source_attributes`。mask 中每个非保留编码必须有且仅有一个映射。
+
+FreiHAND 的试点按本任务需要，统一到手／背景类别，保留每个原生区域编码；不要求给原生细分区域猜测手指名称。需要细部件监督时，尚未核实的部件名称必须保持未知，不能因粗粒度手类别已核实便声称部件语义也已统一。
+
+#### 5.5.2 重叠实例多边形
+
+每发布选择一套任务使用的分割表示，不建立方法/标注集分支。VISOR 的实际试点存在重叠实例多边形，单层标签图不能无损表达。对此补充 `segment/instance_polygons/v1`：`segment/polygons.jsonl` 每帧包含 `frame_index` 和 `instances`，每实例包含 `mask_value` 与 `polygons`（列表，每个多边形为源图像像素坐标顶点序列，保留原始精度）。身份和类别仍由同一 `instances.jsonl` 映射，有效性仍为 `[T]`，通过 schema 区分物理编码。允许多实例重叠，不采用后画覆盖前画。消费者按需栅格化为独立实例二值 mask；不能声称合并的单层 mask 无损。
+
+#### 5.5.3 灰度前景强度
+
+ReInterHand 的官方渲染 mask 为双手合并的灰度前景掩码，实测存在 0–255 的中间值，不是左右手实例编号。登记 `segment/foreground_intensity/v1`：仍存 `segment/mask.mkv`，解码为 `[T,H,W] uint16`，使用 `mkv/uint16_ffv1/v1`，此类数据优先编码为 FFV1 gray8（pixel_format=gray），读取后转 uint16。有效值范围 0–255，归一化值为 `float32(value)/255`；0 为背景端点、255 为前景端点。组件声明 `foreground_category=hand`、`entity_id=null` 和双手合并语义，不使用区域编号映射。这里保留原始渲染 mask 强度，不把它称为经过校准的概率。若任务需要二值 mask，阈值化属于任务层派生操作，记录阈值；共享层不丢弃中间灰度。不同 schema 的数值含义不同，不能将该文件按整数类别图解码。
+
+FreiHAND 训练集的 JPEG mask 使用 `segment/foreground_intensity/v1`，逐像素保留 JPEG 解码后的灰度，`entity_id=hand_right`。该 schema 不限定双手合并：单手或合并范围由 `entity_id` 与 `coverage` 明确声明。评估集整数区域图仍用 `segment/uint16_labels/v1`，两者不能仅凭文件名使用同一种解码语义。
+
+#### 5.5.4 coverage 与有效性
+
+`coverage.kind=full/selected_entities/partial/unknown`。full 指声明的标注范围完整；selected_entities 还应列出目标范围。无论哪种，0 本身不自动成为背景；背景要有明确类别映射，未标注区域不能用于构造负样本。保留源逐类/逐实例 exhaustive 信息，未知不可改写为完整。
+
+三种表示的帧级有效性均为 `[T]`，表示该帧是否提供此分割标注；对应 valid_ref 在组件中登记，仍用小型 bool `.pt` 数组。栅格 mask 必须登记 storage，格式见 3.1.2，所有帧的整数编码或强度逐值完全相同。编码语义由 schema_id 决定，读取器不能仅依据 mask.mkv 文件名选择解码方式。
+
+### 5.6 contact：接触、抓取、区域与距离
+
+#### 5.6.1 relations.jsonl 与 relations_valid.pt
+
+`annotation/contact/relations.jsonl`，schema contact/relations/v1，每帧一行含 frame_index、relations。每条记录含 relation_type（contact/grasp）、subject_id（手槽或登记实体）、object_id、status、source_locator、provenance。status 固定为 positive/negative/unknown/target_unannotated：positive 必须有可解析目标，negative 表示源明确否定该关系，unknown 为不确定，target_unannotated 为确有接触但目标未标注。非 positive 的 object_id 可 null，不能用 null 冒充“不接触”。稀疏源无记录的帧不自动补 negative。
+
+VISOR 的对象 mask ID 必须先按同帧 source_instance_id 解析成 object_id；hand-not-in-contact、none-of-the-above、inconclusive 分别映射 negative、target_unannotated、unknown，保留原始字符串。Dex ycb_grasp_ind 通过 ycb_ids 映射到登记物体，以 relation_type=grasp 保存；不得当作逐顶点接触。
+
+relations 组件的 valid_ref 指向 `relations_valid.pt [T] bool`，表示该帧是否有可用源关系标注。有效帧中可以存在 status=unknown 的明确不确定标注；无源标注的帧为 valid=false、relations=[]。组件仍声明 available/provenance/source_locator。
+
+#### 5.6.2 实体对的顶点区域与距离
+
+已发布的接触区域/距离按参与关系的 subject_id 与 object_id 组织，每对实体保留选定的一套结果，以精确 geometry_ref/topology_id 关联到手或物体/部件；不要求每套数据都有。顶点区域 schema contact/vertex_region/v1：bool `[T,V]` 加同形 valid；顶点距离 schema contact/vertex_distance/v1：float32 `[T,V]`、米、同形 valid，声明 source/target geometry_ref、distance_definition（signed/unsigned、测量到点集或表面等明确源定义）。geometry_ref 必须指向已登记 mesh 或由固定 MANO 模型配置确定的拓扑；拓扑/顶点数变化时按拓扑拆分片段。其他源区域表示只有在实际接入并登记 schema 后才可转换，不能随意栅格化宣称等价。
+
+顶点区域／距离已有存储定义，但尚未完成真实输入试点；接入时必须提供源语义与几何验收证据，不因已经登记 schema 就视为通过。
+
+## 附录：任务范围
+
+不强制收录 2D bbox、完整高频动捕、动作程序、身体 SMPL-X、点云或网络特征。范围内的接触、visibility、选定手关键点、物体几何不能因不便处理而静默丢弃。分割采用的物理编码由第 5.5 节决定，存在重叠时不能以单层标签图声称无损。
+
+不设置 entities_ref/segment/entities.json，不要求与 object_id 重复的 track_id。MediaPipe 不在当前关键点定义中；旧版本迁移必须保留可追溯关系，不能直接删表。手部共享表示不含独立 hand_mesh。
+
+schema.json 是普通注册表，不是 JSON Schema 文档或可执行验收器；定义存在不代表真实数据已验证。验收须保留实测范围与未决项。
+
+本存储约定只更新标准，不代表既有 FFV1／depth.pt 试点已迁移。新发布必须显式写 storage 配置；旧产物按其旧定义读取，不能只改文件后缀或版本标识伪装为合规。
diff --git a/EgoHOS/shared-v1.0/script/EgoHOS_DATA_MAPPING.md b/EgoHOS/shared-v1.0/script/EgoHOS_DATA_MAPPING.md
new file mode 100644
index 0000000000000000000000000000000000000000..8b0af4fc8ea7a2fd3fac55afbf65d6df0390bb60
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/EgoHOS_DATA_MAPPING.md
@@ -0,0 +1,42 @@
+# EgoHOS raw → shared-v1.0
+
+范围：RGB、官方九类语义分割。数据不是已验证连续视频；按独立图片打包，不推断跟踪、时间、相机标定、实例身份或接触延续。
+
+## 来源与实际输入
+
+- 仓库：<https://huggingface.co/datasets/ParityLab/Hand-Datasets-Raw/tree/main/EgoHOS/raw>
+- 固定 HF revision：`8818d04d6cbdc0bf32b6ef949257b82310f16798`。
+- 官方语义：<https://github.com/owenzlz/EgoHOS/blob/cd9bdf427ef1d94582334ebba4188d5aa18433e7/README.md>。
+- 外层 `egohos_dataset.zip` 同时包含内层 ZIP 与解压副本；转换只取外层的 image/label，避免重复收录。
+- 四组原始图片数：train 8993、val 1124、test_indomain 1126、test_outdomain 500。来源是仓库 raw/doc/SOURCES.md；全量脚本另做实际计数校验。
+- 试点 HTTP Range 只取中央目录及选中成员，逐成员 CRC、SHA256 校验。没有下载全包时，不声称验证了整个 ZIP 的 SHA256。
+
+## 字段映射
+
+| raw | shared | 规则 |
+|---|---|---|
+| `<split>/image/<name>` | `observation/rgb.mkv` | JPEG 优先原码流 remux，所有包 SHA256 和全部解码 RGB 核对；不适用时同时验证 FFV1 / libx264rgb CRF=0，取完整视图文件较小者 |
+| RGB 文件可正常解码 | `rgb_valid.pt [T] bool` | 全部 true；坏文件立即失败，不跳过、不补邻帧 |
+| `<split>/label/<stem>.png` | `annotation/segment/mask.mkv` | 保留索引值，不将调色板 RGB 当标签；FFV1 gray16le，全帧逐整数核对 |
+| 类别 0 background | shared mask 9 | shared 0 保留给 unassigned，显式背景不能继续占用 0 |
+| 类别 1、2 | shared mask 1、2 | 解剖学 left/right，entity_id=hand_left/hand_right |
+| 类别 3、4、5 | shared mask 3、4、5 | 左/右/双手一级交互物体类别；entity_id=null |
+| 类别 6、7、8 | shared mask 6、7、8 | 左/右/双手二级交互物体类别；entity_id=null |
+| 以上类别表 | `instances.jsonl` | 每帧一行，semantic 类型；无实例/部件 ID，不捏造实例跟踪 |
+| 源 label 存在且类别/尺寸合法 | `mask_valid.pt [T] bool` | 无额外官方 QA 输入；不能用 mask valid 推断手可见性 |
+| 目录 split | metainfo + frames.split | 两个 test 目录均为 test，source_split 完整区分 indomain/outdomain |
+| 原文件名、归档路径 | `camera/frames.jsonl` | 稳定 sample_id、源 member、源 SHA256、revision、逐帧原定位 |
+| 真实时间/标定缺失 | null / component unavailable | camera model=unknown；不伪造 K、单位外参、FPS |
+
+按 split 和分辨率分组，每片最多 `--chunk` 张。T=1 为 image/single，T>1 为 image/independent；所有 timestamp_ns/clock_domain/source_fps 为 null。MKV 1 FPS 仅作索引，不是采集帧率。view_type=ego。
+
+## 未纳入内容及原因
+
+- contact：官方 README 描述 contact/，但 ParityLab 原始包实际上没有此目录；标注为缺失，不能把交互物体类别推断成逐实体 positive/negative 接触。
+- 几何、MANO、深度、内外参、visibility：该输入无对应监督，缺省，不生成空几何或虚构单位阵。
+- 模型训练源码、权重、演示 GIF：不是观测或标注；原始包和源码仍在 raw。
+- semantic 的 task background 不是所有物体的完备负样本，coverage=partial。
+
+## 验收
+
+`encoding_validation.json` 是每视图逐帧字节验收证明；`doc/validation/report.json` 记录自动检查范围、HTML 样本和全发布文件 SHA256。HTML 展示所有已提供模态及缺失原因。只有人工查看 HTML 后才运行 approve；自动测试不替代人工审核。
diff --git a/EgoHOS/shared-v1.0/script/OakInk-v1_DATA_MAPPING.md b/EgoHOS/shared-v1.0/script/OakInk-v1_DATA_MAPPING.md
new file mode 100644
index 0000000000000000000000000000000000000000..e5708e48b0473606dda17246f1c1f9f4317090a5
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/OakInk-v1_DATA_MAPPING.md
@@ -0,0 +1,55 @@
+# OakInk-v1 raw → shared-v1.0
+
+本脚本的图像范围是 **OakInk-Image**。仓库名称 OakInk-v1 下包含 OakInk-Image v2 图像包，不应误当成 OakInk2。
+
+## 实际可用性与范围
+
+当前 ParityLab HF revision `8818d04d6cbdc0bf32b6ef949257b82310f16798` 有 image/obj.zip、11 个图像分卷、OakBase、OakInk-Shape 等包；**没有 anno_v2_1.zip**。这意味着相机、逐帧关键点/位姿、官方划分均不能从当前图像包恢复。
+
+- 默认缺标注会停止并提示补充文件。
+- 显式 `--allow-rgb-only` 选择缺少逐帧标注的范围：RGB 和输入中已有的静态物体网格；位姿、手几何与 split 缺失均列明。该结果不是完整几何数据集。
+- 补齐 `image/anno`、来源 inventory 后，可通过 `--annotations ... --trust-pickle` 启用几何分支。此分支须在真实标注试点上重新验证并人工确认；随包的合成检查不能代替真实验收。
+
+官方资料：
+
+- <https://github.com/oakink/OakInk/blob/73dc7c85dfd006a4e6fc51fa8182d864a08af1ca/docs/datasets.md>
+- <https://github.com/oakink/OakInk/blob/73dc7c85dfd006a4e6fc51fa8182d864a08af1ca/oikit/oi_image/oi_image.py>
+- <https://github.com/oakink/OakInk/blob/73dc7c85dfd006a4e6fc51fa8182d864a08af1ca/oikit/oi_image/utils.py>
+
+## 映射
+
+| raw | shared | 规则 |
+|---|---|---|
+| `image/stream_release_v2/<seq>/<ts>/<camera>_<frame>.png` | `observation/rgb.mkv` | 不缩放、不旋转；FFV1 RGB 与 libx264rgb CRF=0 两候选全帧 RGB 精确比对，选较小完整视图 |
+| seq/ts/camera/frame | metainfo、frames | 按源数字帧号排序；跨相机共享源 sequence 命名；不凭索引宣称同步 |
+| camera 名 | camera_id | north_east_color、south_east_color、north_west_color、south_west_color，官方 camera index 0–3 对应同顺序 |
+| 外部多相机采集 | view_type=exo | 不是 ego；不因数据名称猜测视角 |
+| 源帧时间未知 | timestamp_ns=null | 日期目录不解析为精确逐帧时间，source_fps=null；sparse video，MKV 1 FPS 仅封装 |
+| `anno/seq_all.json` | annotation_key/sample_id | 官方 `seq/ts, sbj_flag, frame, cam` 拼为 `__`；不能把 sbj_flag 当左右手 |
+| `anno/split*` | split、split_source_locator | 支持 split0 / split1 / split2 / split0_ho，保持官方集合；未分配记录 unknown |
+| `anno/cam_intr/<key>.pkl` | `camera/intrinsic.pt [T,3,3]` | 官方 pinhole K，float32；正焦距、最后一行检查 |
+| `general_info.cam_extr` | `camera/extrinsic.pt [T,4,4]` | T_camera_from_world，列向量，米；SE3 检查，不反转方向 |
+| `anno/hand_j/<key>.pkl` | `keypoints/xyz.pt [T,2,21,3]` | 官方相机系、米，wrist/thumb/index/middle/ring/little 各近端到指尖；顺序与注册 mano21 对齐，保持原数值 |
+| K、hand_j | `keypoints/uv.pt [T,2,21,2]` | 派生 K@xyz 后除以 Z，正 Z 才有效；出图不自动失效，左槽 NaN |
+| 官方 `_hand_side=right` | right 槽 1 | 两人交接两只右手按 person0/person1 分别输出 sequence；绝不冒充同一人的左右手 |
+| `anno/obj_transf/<key>.pkl` | `object/<id>/pose.pt [T,4,4]` | T_camera_from_object；文档误拼 obj_trasnf 也支持，优先官方代码的 obj_transf |
+| `image/obj/<id>.obj` | `object/<id>/assets/<id>.obj` | 遵循官方 load_object：减 `(bbox_min+bbox_max)/2` 后再配位姿；不擅加缩放；源中实际存在的材质/纹理依赖同时复制；若全部引用的 MTL 均缺失，按官方 skip_materials=True 的几何范围移除悬空 mtllib/usemtl，并记录 source_missing_materials；部分缺失则停止供核实 |
+| 序列前缀 obj_id | 物体 metainfo | 官方关联依据，sequence 范围身份，mesh_ref 相对视图根 |
+
+浮点标准值 float32，缺失左手为 NaN，valid 是 CPU bool tensor。已有标注若形状、有限性、K/SE3 检查失败则报错，不静默丢弃。未另输入官方 QA，不能宣称通过独立 QA。各帧标注来源写入 frames.annotation_sources；split 来源也保留。
+
+## 未纳入内容、限制
+
+- **MANO 暂不导出**：general_info 的 hand_pose 是 world-space quaternion；统一规范要求 flat_hand_mean=true 的完整 axis-angle 和“模型顶点所加平移”。仅 quaternion→axis-angle 与复制 hand_tsl 不足以证明中心关节/均值一致；没有实际 MANO 模型与 hand_v 重建比对，不能声称合规转换。补齐该验证后另发布版本。
+- hand_v 不独立发布，按约定手几何只允许由选定 MANO 生成；不以手顶点冒充已验证 MANO。
+- RGB-only 范围没有内外参、手/物位姿、官方 split；图像存在不代表这些标注不存在于原始数据集。
+- 没有逐帧标注时，仍按官方序列前缀关联已下载的 image/obj 静态资产，展示规范坐标系点云；物体位姿 unavailable，不伪造相机系叠加。若资产尚未下载，区分 not downloaded 与源不存在。无对应帧的资产不复制到相机视图。
+- OakInk-Shape 是物体规范坐标系抓取集合，包含虚拟目标；OakBase 是物体知识/部件资产。二者不自动匹配为图像帧、相机系 MANO 或 contact。本次不转换这些分支，原包保留。若要求覆盖它们全部内容，需要独立确定非相机样本的注册表示。
+- 没有独立 visibility / depth / contact 输入，不从 intent、几何距离或关键点是否在图内伪造。
+- 3D 预览用规范物体点与相机系物体点云，显示点数约 3000；这只是预览，发布 OBJ 保留全部顶点/面/依赖。
+
+## 验收
+
+所有 RGB 帧精确验收；标注作形状、有效性、单位/坐标声明、SE3 数值检查。人工必须复核手与物体投影、比例、规范网格中心、person 区分。真实几何试点尚未通过前，不称其为已验收几何发布。
+
+真实试点发现 S10012.obj 引用 model_align.obj.mtl，但 obj.zip 没有提供；本次输出保留几何并显式移除这些悬空材质绑定，不声称输出有原始纹理。
diff --git a/EgoHOS/shared-v1.0/script/README.md b/EgoHOS/shared-v1.0/script/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c18da834eac5843bd9b04b38bd8f415159c1cddb
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/README.md
@@ -0,0 +1,147 @@
+# EgoHOS / OakInk-v1 预处理脚本
+
+以随包 CONTRACT.md 与 schema.json 为规范，输出 `<数据集>/shared-v1.0/{doc,script,data}`。默认只跑试点。**人工查看试点 HTML 后才能全量；全量结束后再次抽样人工确认才能上传。** 本包不是全量处理结果。
+
+## 当前覆盖范围
+
+| 数据集 | 可用路径 | 待补充 |
+|---|---|---|
+| EgoHOS | RGB + 9 类语义分割；四个官方 split；真实源样本可直接 Range 下载试点 | raw 包没有独立 contact 图，无标定/几何 |
+| OakInk-v1 / OakInk-Image | RGB-only 明确选项；可在服务器处理图像分卷 | 当前 HF raw 不含 anno_v2_1.zip；几何分支须补齐后真实试点 |
+| OakInk 几何可选分支 | K、外参、相机系右手 xyz/派生 uv、物体位姿、居中 OBJ 与依赖 | 无真实标注验收前，仅视为待验证适配器；MANO 未实现，不伪造参数 |
+
+OakInk-Shape/OakBase 不作为图像帧转换；具体排除原因见 DATA_MAPPING 文档。FreiHAND 参考链接本次匿名访问返回 401，未以未读内容作实现依据；目录和字段按用户提供的两个约定文件实现。
+
+## 环境
+
+Python 3.10+、FFmpeg（须有 ffv1 与 libx264rgb）、OakInk 分卷解压需要 7z/7zz。CPU 即可。
+
+```bash
+python3 -m venv .venv
+. .venv/bin/activate
+python -m pip install -r requirements.txt
+python test_self.py
+```
+
+如只用 CPU，可先按 PyTorch 官方 CPU 索引安装 torch 再安装 requirements，避免下载 CUDA 依赖。HF 下载/上传使用本机 `hf auth login` 或 HF_TOKEN；不要把 token 写入脚本或发布目录。
+
+## 1. EgoHOS 真实小试点（不下载 4.5 GB 全包）
+
+以下命令在本脚本目录运行，所有输出路径可改到大容量磁盘。extracted 是工作区；不在 raw 内解压。
+
+```bash
+python acquire.py pilot-egohos --out /storage/work/egohos-pilot --per-split 4
+python preprocess.py build --dataset EgoHOS \
+  --input /storage/work/egohos-pilot --out /storage/pilot/EgoHOS/shared-v1.0
+```
+
+浏览 `/storage/pilot/EgoHOS/shared-v1.0/doc/validation/index.html`（静态 HTML，可直接打开）。下拉框能逐帧查看全部试点样本、源 RGB、shared 解码 RGB、mask、叠加、valid、来源、元信息和缺失原因。请核对左右手、背景编码、细物体边界、四个 split。**只有人实际看过并接受后执行：**
+
+```bash
+python preprocess.py approve --release /storage/pilot/EgoHOS/shared-v1.0 --reviewer '审核者姓名'
+```
+
+approve 是人工声明记录，不会自行判断人工是否看过。程序不会自动调用 approve。
+
+## 2. 原始包下载与工作区解压
+
+```bash
+python acquire.py download --dataset EgoHOS --raw /storage/EgoHOS/raw
+python acquire.py extract --dataset EgoHOS --raw /storage/EgoHOS/raw --out /storage/work/egohos-full
+
+python acquire.py download --dataset OakInk-v1 --raw /storage/OakInk-v1/raw
+python acquire.py extract --dataset OakInk-v1 --raw /storage/OakInk-v1/raw --out /storage/work/oakink-full
+```
+
+固定 HF commit，并校验来源文档给出的每包大小/SHA256；原始包保持不变。OakInk `.z01`–`.z10` 与最终 `.zip` 是分卷 ZIP，由 7z 从最终 .zip 读取，不用 `cat` 伪拼接。下载约 124 GB，还需解压、编码候选与输出空间；预先准备数百 GB 空间，命令做磁盘预检但不保证压缩率估计准确。提取失败的目录不自动覆盖，诊断后选新目录重跑。
+
+## 3. OakInk 试点
+
+无需先下载全部 124 GB，也可做有限范围 RGB 小试点（只从首个分卷的前 128 个 PNG 条目中按相机抽样，不代表全数据集随机覆盖）：
+
+```bash
+python acquire.py pilot-oakink-rgb --out /storage/work/oakink-pilot --per-split 2
+python acquire.py fetch-oakink-objects --out /storage/work/oakink-pilot
+python preprocess.py build --dataset OakInk-v1 --input /storage/work/oakink-pilot \
+  --out /storage/pilot/OakInk-v1/shared-v1.0 --allow-rgb-only
+```
+
+完整工作区已解压时，当前 HF 没有 anno，若本次范围只接受现有 RGB：
+
+```bash
+python preprocess.py build --dataset OakInk-v1 \
+  --input /storage/work/oakink-full --out /storage/pilot/OakInk-v1/shared-v1.0 \
+  --allow-rgb-only --per-stratum 4
+```
+
+按 camera×split 固定种子抽样，unknown split 明确保留，不随意划分训练集。该结果没有手关键点或手/物逐帧位姿；如果工作区已有 image/obj，会保留静态网格和规范坐标系预览，仍不能作为完整几何交付。
+
+若已获可信官方 `anno_v2_1.zip`，先用以下命令校验每成员 CRC、记录文件和原包 SHA256，再解压到同一工作区 `image/anno`：
+
+```bash
+python acquire.py extract-annotations --archive /storage/OakInk-v1/raw/data/image/anno_v2_1.zip \
+  --out /storage/work/oakink-full
+```
+
+该命令要求包内根目录为 anno/，发现不同布局会停止，避免猜错路径。然后运行：
+
+```bash
+python preprocess.py build --dataset OakInk-v1 \
+  --input /storage/work/oakink-full --out /storage/pilot/OakInk-v1/shared-v1.0 \
+  --annotations /storage/work/oakink-full/image/anno --trust-pickle --split-scheme split0
+```
+
+`--trust-pickle` 仅用于可信官方标注，因为 pickle 可执行代码。每条来源记录格式（路径相对 --input；SHA256 填真实结果）：
+
+```json
+{"path":"image/anno/seq_all.json","sha256":"实际文件SHA256","source_locator":{"file":"OakInk-v1/raw/data/image/anno_v2_1.zip","archive_sha256":"实际包SHA256","member":"anno/seq_all.json"}}
+```
+
+几何路径不会强行把 sbj_flag 0/1 映射成左右手；两人的右手分别在 person0/person1 序列。检查 canonical 网格、相机系关键点/物体点云以及 2D 叠加。模型来源、量纲或关键点定义无法确认时不要 approve。MANO 需要另做模型配置与顶点重建对照，当前不导出。
+
+## 4. 试点批准后全量
+
+```bash
+python preprocess.py build --dataset EgoHOS --phase full \
+  --input /storage/work/egohos-full --out /storage/EgoHOS/shared-v1.0 \
+  --pilot /storage/pilot/EgoHOS/shared-v1.0
+
+python preprocess.py approve --release /storage/pilot/OakInk-v1/shared-v1.0 --reviewer '审核者姓名'
+python preprocess.py build --dataset OakInk-v1 --phase full \
+  --input /storage/work/oakink-full --out /storage/OakInk-v1/shared-v1.0 \
+  --pilot /storage/pilot/OakInk-v1/shared-v1.0 --allow-rgb-only
+```
+
+OakInk 若试点启用标注，全量也用相同 --annotations、--trust-pickle、--split-scheme，去掉 --allow-rgb-only。chunk、seed、代码、模态范围须与试点一致；变更后须重做试点。输出目录必须不存在。EgoHOS full 强制核对四组完整数量，防止把试点数据当全量。
+
+所有视图均逐帧校验视频/数组；全量 HTML 按 split×camera 随机抽取视图并显示所选视图的每一帧，绝不将人工抽检称为全量人工审核。若要浏览所有帧的所有要素（会生成大量 PNG/HTML）：
+
+```bash
+python validate.py /storage/EgoHOS/shared-v1.0 --all --source-root /storage/work/egohos-full
+```
+
+重新 validate 会使旧人工审核失效。输入、输出、每帧 provenance 与文件 SHA256 均可检查。自动检查只验证实现支持的字段，并不能自动证明数据集原生语义和真实几何正确。
+
+## 5. 最终抽检与上传
+
+打开全量结果的 doc/validation/index.html，再次人工抽检后：
+
+```bash
+python preprocess.py approve --release /storage/EgoHOS/shared-v1.0 --reviewer '审核者姓名'
+python preprocess.py upload --release /storage/EgoHOS/shared-v1.0
+
+python preprocess.py approve --release /storage/OakInk-v1/shared-v1.0 --reviewer '审核者姓名'
+python preprocess.py upload --release /storage/OakInk-v1/shared-v1.0
+```
+
+目标按用户要求固定为 **ParityLab/Hand-Datasets-Raw**，分别是 `EgoHOS/shared-v1.0` 和 **大小写准确的** `OakInk-v1/shared-v1.0`。需要写权限；任何 pilot 都禁止作为正式 shared-v1.0 上传。上传前验证文件哈希，按最多 128 个文件分批提交、文档最后上传；拒绝合并到已经存在的同版本目录，防止留下旧文件。中途失败会保留已提交批次，脚本不会自动覆盖/删除，须核验远端后恢复。未自动上传、未代替用户签署人工审核。
+
+## 输出文件
+
+- `doc/DATA_MAPPING.md` 与兼容约定文件名 `doc/RAW_TO_SHARED.md`：raw 映射、未纳入内容和原因。
+- `doc/validation/index.html`、预览 PNG、`report.json`：视觉与自动检查。
+- `doc/build.json`：完整命令、源 inventory 摘要、选择策略、处理数。
+- `script/`：本次处理脚本和全部必需支持文件；不包含 venv、token 或 MANO 权重。
+- `data/<sequence>/<camera>/`：metainfo、frames、MKV、CPU tensor、已选物体资产。
+
+测试状态与真实试点范围见随包 `TEST_RESULTS.md`。没有真实标注包时，对几何分支的检查只能算合成端到端检查。
diff --git a/EgoHOS/shared-v1.0/script/acquire.py b/EgoHOS/shared-v1.0/script/acquire.py
new file mode 100644
index 0000000000000000000000000000000000000000..4392a83e0ecfa4b79eac1794916b800ee8c02fbb
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/acquire.py
@@ -0,0 +1,325 @@
+#!/usr/bin/env python3
+"""Pinned HF downloads; immutable raw archives; CRC-checked pilot extraction."""
+import argparse
+import hashlib
+import io
+import json
+import random
+import shutil
+import struct
+import subprocess
+import time
+import urllib.error
+import urllib.request
+import zipfile
+import zlib
+from pathlib import Path, PurePosixPath
+
+REPO = 'ParityLab/Hand-Datasets-Raw'
+REVISION = '8818d04d6cbdc0bf32b6ef949257b82310f16798'
+HERE = Path(__file__).resolve().parent
+
+
+def sha(path):
+    with open(path, 'rb') as f:
+        return hashlib.file_digest(f, 'sha256').hexdigest()
+
+
+def safe_path(root, name):
+    p = PurePosixPath(name)
+    if p.is_absolute() or '..' in p.parts or '\\' in name:
+        raise ValueError(f'Unsafe archive path: {name}')
+    target = root.joinpath(*p.parts)
+    if not target.resolve().is_relative_to(root.resolve()):
+        raise ValueError(f'Path escapes extraction root: {name}')
+    return target
+
+
+class RemoteFile(io.RawIOBase):
+    """Small seekable HTTP range reader, suitable for ZIP central directories."""
+    def __init__(self, path, size):
+        self.url = f'https://huggingface.co/datasets/{REPO}/resolve/{REVISION}/{path}'
+        self.size, self.pos = size, 0
+        self.cache_start, self.cache = 0, b''
+
+    def seekable(self):
+        return True
+
+    def tell(self):
+        return self.pos
+
+    def seek(self, offset, whence=0):
+        self.pos = offset + (0 if whence == 0 else self.pos if whence == 1 else self.size)
+        if self.pos < 0:
+            raise ValueError('Negative offset')
+        return self.pos
+
+    def read(self, n=-1):
+        n = min(self.size - self.pos, n if n >= 0 else self.size)
+        if n <= 0:
+            return b''
+        if not (self.cache_start <= self.pos and self.pos + n <= self.cache_start + len(self.cache)):
+            end = min(self.size, self.pos + max(n, 262144)) - 1
+            req = urllib.request.Request(self.url, headers={'Range': f'bytes={self.pos}-{end}'})
+            for attempt in range(4):
+                try:
+                    with urllib.request.urlopen(req, timeout=30) as r:
+                        expected = f'bytes {self.pos}-{end}/{self.size}'
+                        if r.status != 206 or r.headers.get('Content-Range') != expected:
+                            raise RuntimeError(f'Server did not honor byte range {expected}')
+                        self.cache = r.read()
+                        self.cache_start = self.pos
+                        if len(self.cache) != end - self.pos + 1:
+                            raise IOError('Truncated HTTP range')
+                    break
+                except (urllib.error.URLError, TimeoutError, OSError):
+                    if attempt == 3:
+                        raise
+                    time.sleep(attempt + 1)
+        start = self.pos - self.cache_start
+        self.pos += n
+        return self.cache[start:start+n]
+
+
+def checksums(dataset):
+    return json.loads((HERE / 'source_evidence' / f'{dataset}-archive_checksums.json').read_text())['files']
+
+
+def extract_zip(archive, target, origin, selected=None):
+    records = []
+    with zipfile.ZipFile(archive) as z:
+        for info in z.infolist():
+            if info.is_dir() or (selected is not None and info.filename not in selected):
+                continue
+            # Do not create symlinks/devices. Treat regular members only.
+            mode = info.external_attr >> 16
+            if mode & 0o170000 not in (0, 0o100000):
+                raise ValueError(f'Non-regular archive member: {info.filename}')
+            p = safe_path(target, info.filename)
+            p.parent.mkdir(parents=True, exist_ok=True)
+            if shutil.disk_usage(p.parent).free < info.file_size + 256 * 1024**2:
+                raise RuntimeError('Insufficient space for next uncompressed member')
+            with z.open(info) as src, p.open('xb') as dst:
+                shutil.copyfileobj(src, dst)  # ZipFile checks member CRC at EOF.
+            records.append({'path': p.relative_to(target).as_posix(), 'sha256': sha(p),
+                            'source_locator': dict(origin, member=info.filename)})
+    return records
+
+
+def download(dataset, root):
+    from huggingface_hub import hf_hub_download
+    entries = [e for e in checksums(dataset) if '#' not in e['path']]
+    need = sum(e['size'] for e in entries if not (root / e['path']).exists())
+    root.mkdir(parents=True, exist_ok=True)
+    if shutil.disk_usage(root).free < need + 5 * 1024**3:
+        raise RuntimeError(f'Insufficient disk; raw downloads need {need / 1e9:.1f} GB plus reserve')
+    for e in entries:
+        dst = root / e['path']
+        if dst.exists():
+            if dst.stat().st_size != e['size'] or sha(dst) != e['sha256']:
+                raise RuntimeError(f'Existing raw file differs: {dst}')
+            print(f'Already verified: {dst}', flush=True)
+            continue
+        remote = f'{dataset}/raw/{e["path"]}'
+        p = Path(hf_hub_download(REPO, remote, repo_type='dataset', revision=REVISION,
+                                local_dir=root / '_hf'))
+        if p.stat().st_size != e['size'] or sha(p) != e['sha256']:
+            raise RuntimeError(f'Archive checksum mismatch: {remote}')
+        dst.parent.mkdir(parents=True, exist_ok=True)
+        if not dst.exists():
+            p.replace(dst)
+        elif sha(dst) != e['sha256']:
+            raise RuntimeError(f'Existing raw file differs: {dst}')
+        print(dst, flush=True)
+
+
+def pilot_ego(target, count, seed):
+    e = checksums('EgoHOS')[0]
+    remote = f'EgoHOS/raw/{e["path"]}'
+    f = RemoteFile(remote, e['size'])
+    with zipfile.ZipFile(f) as z:
+        images = [n for n in z.namelist() if '/image/' in n and n.lower().endswith(('.jpg', '.jpeg', '.png'))]
+        chosen = []
+        for split in ('train', 'val', 'test_indomain', 'test_outdomain'):
+            pool = sorted(n for n in images if f'/{split}/image/' in n)
+            chosen.extend(random.Random(seed).sample(pool, min(count, len(pool))))
+        if not chosen:
+            raise RuntimeError('Expected outer ZIP image layout not found')
+        names = set(z.namelist())
+        members = set(chosen)
+        for name in chosen:
+            label = str(PurePosixPath(name.replace('/image/', '/label/')).with_suffix('.png'))
+            if label not in names:
+                raise RuntimeError(f'Label missing: {label}')
+            members.add(label)
+    records = extract_zip(f, target, {'repo': REPO, 'revision': REVISION, 'file': remote,
+                          'archive_sha256_expected': e['sha256'],
+                          'verification': 'selected member CRC + SHA256; full archive NOT downloaded/verified'}, members)
+    return records
+
+
+def pilot_oak_rgb(target, count, seed):
+    """Pilot-only: sample first 128 PNG local entries from volume z01, stratified by camera.
+
+    This is NOT a whole-dataset random sample and cannot substitute for a full download.
+    Known split ZIP uses local CRC/sizes; other layouts fail closed.
+    """
+    entry = next(e for e in checksums('OakInk-v1') if e['path'].endswith('.z01'))
+    remote = f'OakInk-v1/raw/{entry["path"]}'
+    f = RemoteFile(remote, entry['size'])
+    if f.read(4) != b'PK\x07\x08':
+        raise ValueError('Unexpected split ZIP signature')
+    pool = {}
+    seen = 0
+    while seen < 128:
+        header = f.read(30)
+        if len(header) != 30 or header[:4] != b'PK\x03\x04':
+            raise ValueError('Unsupported split ZIP local header; use full download + 7z')
+        _, version, flags, method, _, _, crc, compressed, size, nlen, xlen = struct.unpack('<4s5H3I2H', header)
+        name = f.read(nlen).decode('utf-8' if flags & 2048 else 'cp437')
+        f.read(xlen)
+        if flags & 9 or compressed == 0xffffffff or method not in (0, 8):
+            raise ValueError('Unsupported encrypted/descriptor/ZIP64 local member; use full extraction')
+        offset = f.tell()
+        if offset + compressed > entry['size']:
+            raise ValueError('Pilot member crosses volume; use full extraction')
+        if name.endswith('.png'):
+            seen += 1
+            camera = PurePosixPath(name).stem.rsplit('_', 1)[0]
+            pool.setdefault(camera, []).append((name, offset, compressed, size, method, crc))
+        f.seek(offset + compressed)
+    rng = random.Random(seed)
+    records = []
+    for camera in sorted(pool):
+        for name, offset, compressed, size, method, crc in rng.sample(pool[camera], min(count, len(pool[camera]))):
+            f.seek(offset)
+            packed = f.read(compressed)
+            content = packed if method == 0 else zlib.decompress(packed, -15)
+            if len(content) != size or zlib.crc32(content) & 0xffffffff != crc:
+                raise ValueError(f'Pilot member CRC/size mismatch: {name}')
+            path = safe_path(target / 'image', name)
+            path.parent.mkdir(parents=True, exist_ok=True)
+            path.write_bytes(content)
+            records.append({'path': path.relative_to(target).as_posix(), 'sha256': sha(path),
+                'source_locator': {'repo': REPO, 'revision': REVISION, 'file': remote, 'member': name,
+                    'compressed_member_offset': offset, 'archive_sha256_expected': entry['sha256'],
+                    'verification': 'selected member CRC + SHA256; complete volume NOT verified',
+                    'selection_scope': 'first 128 PNG entries in z01; camera-stratified, NOT global random'}})
+    return records
+
+
+def extract_local(dataset, raw, target):
+    entries = [e for e in checksums(dataset) if '#' not in e['path']]
+    for e in entries:
+        p = raw / e['path']
+        if not p.exists() or p.stat().st_size != e['size'] or sha(p) != e['sha256']:
+            raise RuntimeError(f'Download/verify raw first: {p}')
+    records = []
+    for e in entries:
+        p = raw / e['path']
+        if p.suffix != '.zip':
+            continue
+        origin = {'repo': REPO, 'revision': REVISION, 'file': f'{dataset}/raw/{e["path"]}',
+                  'archive_sha256': e['sha256']}
+        if dataset == 'EgoHOS':
+            with zipfile.ZipFile(p) as z:
+                members = {n for n in z.namelist() if '/image/' in n or '/label/' in n or '/contact/' in n}
+            records.extend(extract_zip(p, target, origin, members))
+        elif '/stream_zipped/' in e['path']:
+            tool = shutil.which('7zz') or shutil.which('7z')
+            if not tool:
+                raise RuntimeError('Install 7zip (7zz or 7z) for OakInk split ZIP volumes')
+            # Official immutable archives were SHA256 checked above. Never concatenate volumes.
+            dest = target / 'image'
+            subprocess.run([tool, 'x', '-y', '-o' + str(dest), str(p)], check=True)
+            volumes = [dict(file=f'{dataset}/raw/{v["path"]}', sha256=v['sha256'])
+                       for v in entries if '/stream_zipped/' in v['path']]
+            for image in sorted((dest / 'stream_release_v2').rglob('*.png')):
+                records.append({'path': image.relative_to(target).as_posix(), 'sha256': sha(image),
+                                'source_locator': dict(origin, volumes=volumes,
+                                                       member=image.relative_to(dest).as_posix())})
+        else:
+            dest = target / ('image' if '/image/' in e['path'] else 'shape' if '/shape/' in e['path'] else '')
+            batch = extract_zip(p, dest, origin)
+            for r in batch:
+                r['path'] = (dest.relative_to(target) / r['path']).as_posix()
+            records.extend(batch)
+    return records
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument('command', choices=['download', 'extract', 'pilot-egohos', 'pilot-oakink-rgb', 'extract-annotations', 'fetch-oakink-objects'])
+    p.add_argument('--dataset', choices=['EgoHOS', 'OakInk-v1'], default='EgoHOS')
+    p.add_argument('--raw', type=Path)
+    p.add_argument('--out', type=Path)
+    p.add_argument('--archive', type=Path, help='Official anno_v2_1.zip for extract-annotations')
+    p.add_argument('--per-split', type=int, default=4)
+    p.add_argument('--seed', type=int, default=20260928)
+    a = p.parse_args()
+    if a.command == 'fetch-oakink-objects':
+        if not a.out or not (a.out / 'sources.jsonl').is_file():
+            p.error('Existing extracted --out with sources.jsonl required')
+        entry = next(e for e in checksums('OakInk-v1') if e['path'] == 'data/image/obj.zip')
+        remote = 'OakInk-v1/raw/' + entry['path']
+        archive = a.out.parent / 'raw-pilot' / remote
+        archive.parent.mkdir(parents=True, exist_ok=True)
+        if not archive.exists():
+            archive.write_bytes(RemoteFile(remote, entry['size']).read())
+        if sha(archive) != entry['sha256']:
+            raise ValueError('Object archive checksum mismatch')
+        rows = extract_zip(archive, a.out / 'image', {'repo': REPO, 'revision': REVISION,
+            'file': remote, 'archive_sha256': entry['sha256']})
+        with (a.out / 'sources.jsonl').open('a') as f:
+            for r in rows:
+                r['path'] = 'image/' + r['path']
+                f.write(json.dumps(r, ensure_ascii=False) + '\n')
+        print(f'Imported {len(rows)} object assets; original zip retained at {archive}')
+        return
+    if a.command == 'extract-annotations':
+        if not a.archive or not a.out or not (a.out / 'sources.jsonl').is_file():
+            p.error('--archive and existing extracted --out with sources.jsonl required')
+        with zipfile.ZipFile(a.archive) as z:
+            regular = [n for n in z.namelist() if not n.endswith('/')]
+            if not regular or not all(n.startswith('anno/') for n in regular):
+                p.error('Expected archive rooted at anno/; inspect actual layout before importing')
+            if any(safe_path(a.out / 'image', n).exists() for n in regular):
+                p.error('Annotation target already exists; refusing to overwrite')
+        rows = extract_zip(a.archive, a.out / 'image', {'file': str(a.archive.resolve()),
+            'archive_sha256': sha(a.archive), 'origin': 'user-supplied official annotation archive; not present in pinned HF raw'})
+        with (a.out / 'sources.jsonl').open('a') as f:
+            for r in rows:
+                r['path'] = 'image/' + r['path']
+                f.write(json.dumps(r, ensure_ascii=False) + '\n')
+        print(f'Imported {len(rows)} annotation files with provenance')
+        return
+    if a.command == 'download':
+        if not a.raw:
+            p.error('--raw required')
+        download(a.dataset, a.raw)
+        return
+    if not a.out or a.out.exists():
+        p.error('--out must be a new directory (raw is never modified)')
+    if a.command == 'extract' and not a.raw:
+        p.error('--raw required')
+    if a.per_split < 1:
+        p.error('--per-split must be positive')
+    a.out.mkdir(parents=True)
+    if a.command == 'pilot-egohos':
+        a.dataset = 'EgoHOS'
+        records = pilot_ego(a.out, a.per_split, a.seed)
+    elif a.command == 'pilot-oakink-rgb':
+        a.dataset = 'OakInk-v1'
+        records = pilot_oak_rgb(a.out, a.per_split, a.seed)
+    else:
+        records = extract_local(a.dataset, a.raw, a.out)
+    with (a.out / 'sources.jsonl').open('w') as f:
+        for r in records:
+            f.write(json.dumps(r, ensure_ascii=False) + '\n')
+    (a.out / 'acquisition.json').write_text(json.dumps({'dataset': a.dataset, 'revision': REVISION,
+        'command': a.command, 'seed': a.seed, 'files': len(records)}, indent=2))
+    print(f'{len(records)} extracted files -> {a.out}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/EgoHOS/shared-v1.0/script/media.py b/EgoHOS/shared-v1.0/script/media.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b518a8b9a0a244bde70fd66f74aa7f774e973d7
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/media.py
@@ -0,0 +1,155 @@
+"""Lossless media I/O, bounded by one frame; no resizing or automatic rotation."""
+import hashlib
+import io
+import json
+import subprocess
+import time
+from fractions import Fraction
+from pathlib import Path
+
+import av
+import numpy as np
+from PIL import Image
+
+
+def rgb(path):
+    with Image.open(path) as im:
+        if im.mode not in ('RGB', 'L', 'P', 'RGBA'):
+            raise ValueError(f'Unsupported source bit depth/mode {im.mode}: {path}')
+        if (im.mode == 'RGBA' or 'transparency' in im.info) and im.convert('RGBA').getextrema()[3] != (255, 255):
+            raise ValueError(f'Nonopaque source alpha cannot silently be discarded: {path}')
+    with av.open(str(path)) as c:
+        frames = list(c.decode(video=0))
+        if len(frames) != 1 or any(x.bits > 8 for x in frames[0].format.components):
+            raise ValueError(f'Expected one 8-bit image: {path}')
+        return frames[0].to_ndarray(format='rgb24')
+
+
+def mask(path):
+    with Image.open(path) as im:
+        a = np.asarray(im)
+    if a.ndim != 2 or not np.isin(a, np.arange(9)).all():
+        raise ValueError(f'Unexpected EgoHOS label palette/values: {path}')
+    # Shared 0 means unassigned, so explicit source background 0 becomes 9.
+    return np.where(a == 0, 9, a).astype(np.uint16)
+
+
+def decode(path, labels=False):
+    with av.open(str(path)) as c:
+        for frame in c.decode(video=0):
+            yield frame.to_ndarray(format='gray16le' if labels else 'rgb24')
+
+
+def verify(path, sources, loader, labels=False):
+    start = time.monotonic()
+    it = iter(sources)
+    hashes = []
+    for frame in decode(path, labels):
+        source = next(it, None)
+        if source is None:
+            raise ValueError(f'Extra video frame in {path}')
+        original = loader(source)
+        if not np.array_equal(original, frame):
+            raise ValueError(f'Lossless round-trip mismatch at frame {len(hashes)}: {path}')
+        hashes.append(hashlib.sha256(frame.tobytes()).hexdigest())
+    if next(it, None) is not None or not hashes:
+        raise ValueError(f'Missing video frames: {path}')
+    return {'frames': len(hashes), 'all_decoded_frames_exact': True,
+            'ordered_decoded_sha256': hashes, 'decode_verify_seconds': time.monotonic() - start}
+
+
+def encode_ffmpeg(paths, target, loader, size, encoder, labels=False):
+    pix = 'gray16le' if labels else 'rgb24'
+    options = ['-c:v', 'ffv1', '-level', '3', '-pix_fmt', pix if labels else 'bgr0']
+    if encoder == 'libx264rgb':
+        options = ['-c:v', 'libx264rgb', '-crf', '0', '-preset', 'medium', '-g', '64', '-pix_fmt', 'rgb24']
+    command = ['ffmpeg', '-hide_banner', '-loglevel', 'error', '-y', '-f', 'rawvideo',
+               '-pixel_format', pix, '-video_size', f'{size[0]}x{size[1]}', '-framerate', '1',
+               '-i', 'pipe:0', '-an', *options, str(target)]
+    # stderr goes to a file, avoiding a full pipe deadlock on bad inputs.
+    with target.with_suffix('.log').open('wb') as log:
+        p = subprocess.Popen(command, stdin=subprocess.PIPE, stderr=log)
+        try:
+            for path in paths:
+                a = loader(path)
+                if list(a.shape[1::-1]) != size:
+                    raise ValueError(f'Changing image size: {path}')
+                p.stdin.write(a.astype('<u2' if labels else 'u1', copy=False).tobytes())
+            p.stdin.close()
+            if p.wait() != 0:
+                raise RuntimeError(target.with_suffix('.log').read_text())
+        except BaseException:
+            p.kill()
+            p.wait()
+            raise
+    target.with_suffix('.log').unlink()
+    return command
+
+
+def encode_copy(paths, target):
+    with av.open(str(paths[0])) as src, av.open(str(target), 'w', format='matroska') as dst:
+        stream = dst.add_stream_from_template(src.streams.video[0])
+        stream.time_base = Fraction(1, 1000)
+        for i, path in enumerate(paths):
+            packet = av.Packet(Path(path).read_bytes())
+            packet.stream = stream
+            packet.pts = packet.dts = i * 1000
+            packet.duration = 1000
+            packet.time_base = Fraction(1, 1000)
+            dst.mux(packet)
+    with av.open(str(target)) as c:
+        actual = [hashlib.sha256(bytes(p)).hexdigest() for p in c.demux(video=0) if p.size]
+    expected = [hashlib.sha256(Path(p).read_bytes()).hexdigest() for p in paths]
+    if actual != expected:
+        raise ValueError('JPEG packets changed during remux')
+    return {'all_jpeg_packets_exact': True, 'packet_count': len(actual), 'ordered_packet_sha256': actual}
+
+
+def encode(paths, target, labels=False):
+    target.parent.mkdir(parents=True, exist_ok=True)
+    loader = mask if labels else rgb
+    size = list(loader(paths[0]).shape[1::-1])
+    candidates, failed = [], []
+    choices = ['ffv1'] if labels else ['ffv1', 'libx264rgb']
+    if not labels and all(Path(p).suffix.lower() in ('.jpg', '.jpeg') for p in paths):
+        choices.insert(0, 'copy')
+    for encoder in choices:
+        path = target.with_name(target.stem + '.' + encoder + '.mkv')
+        start = time.monotonic()
+        try:
+            extra = encode_copy(paths, path) if encoder == 'copy' else {'command': encode_ffmpeg(paths, path, loader, size, encoder, labels)}
+            elapsed = time.monotonic() - start
+            proof = verify(path, paths, loader, labels)
+            candidates.append({'encoder': encoder, 'path': path, 'bytes': path.stat().st_size,
+                               'encode_seconds': elapsed, **proof, **extra})
+            if encoder == 'copy':
+                break  # Contract prioritizes exact source packet copy.
+        except (ValueError, RuntimeError, av.FFmpegError) as e:
+            failed.append({'encoder': encoder, 'error': str(e)})
+            path.unlink(missing_ok=True)
+    if not candidates:
+        raise RuntimeError(f'No exact lossless encoder candidate: {failed}')
+    chosen = min(candidates, key=lambda x: x['bytes'])
+    chosen['path'].replace(target)
+    for c in candidates:
+        c['path'].unlink(missing_ok=True)
+        c.pop('path')
+    with av.open(str(target)) as container:
+        s = container.streams.video[0]
+        actual = {'codec': s.codec_context.name, 'pixel_format': s.codec_context.format.name,
+                  'encoded_size': [s.width, s.height]}
+    enc = chosen['encoder']
+    profile = ('mkv/uint16_ffv1/v1' if labels else {'copy': 'mkv/mjpeg_copy/v1',
+               'ffv1': 'mkv/ffv1_rgb/v1', 'libx264rgb': 'mkv/h264_rgb_lossless/v1'}[enc])
+    storage = {'profile': profile, **actual, 'storage_fps': 1, 'timebase_role': 'index_only',
+               'source_lossy': False if labels else (True if all(Path(p).suffix.lower() in ('.jpg', '.jpeg') for p in paths) else 'unknown'),
+               'decoder': {'name': 'PyAV', 'version': av.__version__,
+                           'libraries': {k: list(v) for k, v in av.library_versions.items()},
+                           'output': 'gray16le' if labels else 'rgb24', 'rotation': 'none',
+                           'color_interpretation': 'libswscale source metadata/defaults; identical source/target decoder',
+                           'source_color_space': 'unknown'},
+               'encoder_parameters': {'name': enc, 'stream_copy': enc == 'copy',
+                   'ffmpeg_version': subprocess.check_output(['ffmpeg', '-version'], text=True).splitlines()[0],
+                   'crf': 0 if enc == 'libx264rgb' else None, 'gop': 64 if enc == 'libx264rgb' else None}}
+    return storage, {'selected': enc, 'candidates': candidates, 'failed_candidates': failed,
+                     'pixel_coverage': 'ALL frames, count and order', 'random_access': 'sequential decode from beginning'}
diff --git a/EgoHOS/shared-v1.0/script/preprocess.py b/EgoHOS/shared-v1.0/script/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e63aca155c9850aa0d4be2bac7a26bba37cdec9
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/preprocess.py
@@ -0,0 +1,574 @@
+#!/usr/bin/env python3
+"""EgoHOS / OakInk-Image -> hand-warehouse/1.0. Full runs require pilot review."""
+import argparse
+from collections import defaultdict
+from functools import lru_cache
+import hashlib
+import json
+import os
+import pickle
+import random
+import re
+import shlex
+import shutil
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+import torch
+
+from acquire import sha, safe_path
+from media import encode
+
+HERE = Path(__file__).resolve().parent
+CAMS = ['north_east_color', 'south_east_color', 'north_west_color', 'south_west_color']
+LABELS = ['background', 'left_hand', 'right_hand', 'first_order_left', 'first_order_right',
+          'first_order_both', 'second_order_left', 'second_order_right', 'second_order_both']
+
+
+def dump(path, data):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(data, indent=2, ensure_ascii=False, allow_nan=False) + '\n')
+
+
+def jsonl(path, rows):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open('w') as f:
+        for row in rows:
+            f.write(json.dumps(row, ensure_ascii=False, allow_nan=False) + '\n')
+
+
+def load(path):
+    return json.loads(path.read_text())
+
+
+def tensor(root, ref, value):
+    p = root / ref
+    p.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(torch.from_numpy(np.asarray(value).copy()).cpu(), p)
+
+
+def component(schema, ref, axes, valid, vaxes, source, method, kind='format_conversion', **extra):
+    return {'available': True, 'schema_id': schema, 'data_ref': ref, 'axes': axes,
+            'valid_ref': valid, 'valid_axes': vaxes, 'provenance': {'kind': kind, 'method': method},
+            'source_locator': source, **extra}
+
+
+def source_index(root):
+    path = root / 'sources.jsonl'
+    if not path.exists():
+        raise ValueError('Missing sources.jsonl; run acquire.py first or create a provenance inventory as documented')
+    result = {}
+    for line in path.read_text().splitlines():
+        row = json.loads(line)
+        if row['path'] in result:
+            raise ValueError(f'Duplicate source path: {row["path"]}')
+        safe_path(root, row['path'])
+        result[row['path']] = row
+    return result
+
+
+@lru_cache(maxsize=64)
+def source_digest(path, size, mtime_ns):
+    return sha(path)
+
+
+def locator(path, root, index):
+    key = path.relative_to(root).as_posix()
+    row = index.get(key)
+    if row is None:
+        raise ValueError(f'Missing provenance for {key}; add it to sources.jsonl')
+    stat = path.stat()
+    if source_digest(path, stat.st_size, stat.st_mtime_ns) != row['sha256']:
+        raise ValueError(f'Source changed: {path}')
+    return dict(row['source_locator'], extracted_path=key, extracted_sha256=row['sha256'])
+
+
+@lru_cache(maxsize=128)
+def find_one(root, name):
+    matches = [p for p in root.rglob(name) if p.is_dir()]
+    if len(matches) != 1:
+        raise ValueError(f'Expected one {name}/ directory, found {len(matches)}')
+    return matches[0]
+
+
+def inventory_ego(root):
+    rows = []
+    for split in ('train', 'val', 'test_indomain', 'test_outdomain'):
+        d = find_one(root, split)
+        images = sorted(p for p in (d / 'image').iterdir() if p.suffix.lower() in ('.jpg', '.jpeg', '.png'))
+        for p in images:
+            label = d / 'label' / (p.stem + '.png')
+            if not label.is_file():
+                raise ValueError(f'Image without segmentation: {p}')
+            rows.append({'image': p, 'mask': label, 'split': 'test' if split.startswith('test_') else split,
+                         'source_split': split, 'camera_id': 'ego', 'sample_id': split + '/' + p.name})
+    if not rows:
+        raise ValueError('No EgoHOS images')
+    return rows
+
+
+def inventory_oak(root, annotations, split_scheme):
+    stream = find_one(root, 'stream_release_v2')
+    rows = []
+    if annotations:
+        info = load(annotations / 'seq_all.json')
+        splits = {}
+        for split in ('train', 'val', 'test'):
+            relative = (f'split_train_val/{split_scheme}/example_split_{split}.json' if split != 'test'
+                        else f'split/{split_scheme}/seq_test.json')
+            file = annotations / relative
+            if not file.is_file():
+                raise ValueError(f'Official split missing: {file}')
+            for x in load(file):
+                key = tuple(x)
+                if key in splits:
+                    raise ValueError('Conflicting official splits')
+                splits[key] = (split, relative)
+        for x in info:
+            seq, subject, frame, camera = x
+            if camera not in range(4) or subject not in (0, 1):
+                raise ValueError(f'Unexpected official sample key: {x}')
+            name = '__'.join(map(str, x)).replace('/', '__')
+            split, split_ref = splits.get(tuple(x), ('unknown', None))
+            rows.append({'image': stream / seq / f'{CAMS[camera]}_{frame}.png',
+                'camera_id': CAMS[camera], 'sequence': seq, 'source_frame_id': str(frame),
+                'sample_id': name, 'subject': subject, 'split': split, 'source_split': split,
+                'annotation_key': name, 'split_ref': split_ref})
+    else:
+        for p in sorted(stream.rglob('*.png')):
+            m = re.fullmatch(r'(north_east|south_east|north_west|south_west)_color_(\d+)', p.stem)
+            if not m or len(p.relative_to(stream).parts) != 3:
+                raise ValueError(f'Unexpected OakInk image path: {p}')
+            rows.append({'image': p, 'camera_id': m[1] + '_color',
+                'sequence': p.parent.relative_to(stream).as_posix(), 'source_frame_id': m[2],
+                'sample_id': p.relative_to(stream).as_posix(), 'subject': None,
+                'split': 'unknown', 'source_split': 'unknown'})
+    if not rows:
+        raise ValueError('No OakInk images')
+    return rows
+
+
+def select_rows(rows, dataset, phase, count, seed):
+    if phase == 'full':
+        return rows
+    strata = defaultdict(list)
+    for r in rows:
+        strata[r['source_split'] if dataset == 'EgoHOS' else (r['split'], r['camera_id'])].append(r)
+    rng = random.Random(seed)
+    return [r for key in sorted(strata) for r in rng.sample(strata[key], min(count, len(strata[key])))]
+
+
+def groups(rows, dataset, chunk):
+    buckets = defaultdict(list)
+    for r in rows:
+        with Image.open(r['image']) as im:
+            size = im.size
+        if dataset == 'EgoHOS':
+            key = (r['source_split'], r['camera_id'], size)
+        else:
+            # Source frame-number intervals keep cameras under a common sequence namespace.
+            key = (r['sequence'], r['subject'], int(r['source_frame_id']) // chunk, r['camera_id'], size)
+        buckets[key].append(r)
+    result = []
+    for key in sorted(buckets, key=str):
+        batch = sorted(buckets[key], key=lambda r: (int(r.get('source_frame_id', 0)), r['sample_id']))
+        if dataset == 'EgoHOS':
+            for start in range(0, len(batch), chunk):
+                sequence = f'{key[0]}_{key[2][0]}x{key[2][1]}_{start//chunk:06d}'
+                result.append((sequence, key[1], key[2], batch[start:start+chunk]))
+        else:
+            name = key[0].replace('/', '__')
+            person = '' if key[1] is None else f'__person{key[1]}'
+            sequence = f'{name}{person}__window{key[2]:06d}_{key[4][0]}x{key[4][1]}'
+            result.append((sequence, key[3], key[4], batch))
+    return result
+
+
+def read_pickle(path):
+    # Only called with explicit --trust-pickle for source annotation archives.
+    with path.open('rb') as f:
+        return pickle.load(f)
+
+
+def numeric(value, shape, name):
+    if isinstance(value, torch.Tensor):
+        value = value.detach().cpu().numpy()
+    a = np.asarray(value, dtype=np.float32)
+    if a.shape != shape or not np.isfinite(a).all():
+        raise ValueError(f'{name}: expected finite {shape}, got {a.shape}')
+    return a
+
+
+def se3(value, name):
+    a = numeric(value, (4, 4), name)
+    if not (np.allclose(a[3], [0, 0, 0, 1], atol=1e-5) and
+            np.allclose(a[:3, :3].T @ a[:3, :3], np.eye(3), atol=1e-4) and
+            np.isclose(np.linalg.det(a[:3, :3]), 1, atol=1e-4)):
+        raise ValueError(f'Invalid rigid transform: {name}')
+    return a
+
+
+def copy_mesh(source, dst, root, index):
+    lines = source.read_text().splitlines()
+    materials = [n for line in lines if line.startswith('mtllib ') for n in shlex.split(line)[1:]]
+    missing_materials = [n for n in materials if not safe_path(source.parent, n).is_file()]
+    if missing_materials and len(missing_materials) != len(materials):
+        raise ValueError(f'Partly missing OBJ materials need review: {source}: {missing_materials}')
+    # Official OakInk loader uses skip_materials=True; dangling source references are documented.
+    if missing_materials:
+        lines = [line for line in lines if not line.startswith(('mtllib ', 'usemtl '))]
+    vertices = np.array([[float(v) for v in line.split()[1:4]] for line in lines if line.startswith('v ')])
+    if vertices.ndim != 2 or vertices.shape[1] != 3 or not np.isfinite(vertices).all():
+        raise ValueError(f'Invalid OBJ: {source}')
+    center = (vertices.min(0) + vertices.max(0)) / 2
+    shifted = iter(vertices - center)
+    output = []
+    for line in lines:
+        if line.startswith('v '):
+            v = next(shifted)
+            line = 'v ' + ' '.join(format(x, '.12g') for x in v) + (' ' + ' '.join(line.split()[4:]) if len(line.split()) > 4 else '')
+        output.append(line)
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    dst.write_text('\n'.join(output) + '\n')
+    sources = [locator(source, root, index)]
+    for line in lines:
+        if line.startswith('mtllib '):
+            for name in shlex.split(line)[1:]:
+                mtl = safe_path(source.parent, name)
+                dest = safe_path(dst.parent, name)
+                dest.parent.mkdir(parents=True, exist_ok=True)
+                shutil.copy2(mtl, dest)
+                sources.append(locator(mtl, root, index))
+                for entry in mtl.read_text().splitlines():
+                    if entry.strip().startswith(('map_', 'bump ', 'disp ', 'decal ', 'norm ')):
+                        texture = shlex.split(entry)[-1]
+                        src = safe_path(mtl.parent, texture)
+                        to = safe_path(dest.parent, texture)
+                        to.parent.mkdir(parents=True, exist_ok=True)
+                        shutil.copy2(src, to)
+                        sources.append(locator(src, root, index))
+    return center.tolist(), sources, missing_materials
+
+
+def oak_object_asset(view, rows, meta, root, index):
+    objid = rows[0]['sequence'].split('/')[0].split('_')[0]
+    mesh = find_one(root, 'obj') / (objid + '.obj')
+    ref = f'annotation/object/{objid}/assets/{objid}.obj'
+    center, sources, missing_materials = copy_mesh(mesh, view / ref, root, index)
+    meta['components']['object_mesh'] = {'available': True, 'schema_id': 'mesh/obj/v1', 'data_ref': ref,
+        'axes': [], 'valid': True, 'unit': 'm', 'source_locator': sources,
+        'provenance': {'kind': 'format_conversion', 'method': 'subtract (min(vertices)+max(vertices))/2, official oikit load_object; remove dangling material bindings only if all referenced MTL files are absent in source',
+                       'subtracted_center': center, 'source_missing_materials': missing_materials}}
+    dump(view / f'annotation/object/{objid}/metainfo.json', {'object_id': objid, 'identity_scope': 'sequence',
+        'scope_id': meta['sequence_id'], 'source_object_id': objid, 'has_model': True,
+        'mesh_ref': ref, 'source_locator': sources, 'association_basis': 'official sequence prefix object ID'})
+
+
+def oak_geometry(view, rows, frames, meta, root, index, anno, split_scheme):
+    count = len(rows)
+    keys = [r['annotation_key'] for r in rows]
+    arrays = {'intrinsic': [], 'extrinsic': [], 'xyz': [], 'pose': []}
+    objid = rows[0]['sequence'].split('/')[0].split('_')[0]
+    for i, key in enumerate(keys):
+        sources = {}
+        for name in ('cam_intr', 'hand_j', 'general_info', 'obj_transf'):
+            p = anno / name / (key + '.pkl')
+            # Some documentation spells this directory obj_trasnf; code uses obj_transf.
+            if name == 'obj_transf' and not p.exists():
+                p = anno / 'obj_trasnf' / (key + '.pkl')
+            sources[name] = locator(p, root, index)
+            sources[name]['record'] = key
+            value = read_pickle(p)
+            if name == 'cam_intr':
+                k = numeric(value, (3, 3), name)
+                if not np.allclose(k[2], [0, 0, 1]) or min(k[0, 0], k[1, 1]) <= 0:
+                    raise ValueError('Invalid pinhole K')
+                arrays['intrinsic'].append(k)
+            elif name == 'hand_j':
+                arrays['xyz'].append(numeric(value, (21, 3), name))
+            elif name == 'obj_transf':
+                arrays['pose'].append(se3(value, name))
+            else:
+                arrays['extrinsic'].append(se3(value['cam_extr'], 'T_camera_from_world'))
+        frames[i]['annotation_sources'] = sources
+        frames[i]['source_subject_flag'] = rows[i]['subject']
+        if rows[i]['split_ref']:
+            frames[i]['split_source_locator'] = locator(anno / rows[i]['split_ref'], root, index)
+        frames[i]['annotation_matches'] = {name: {'source_frame_id': key, 'source_timestamp_ns': None,
+            'clock_domain': None, 'match_method': 'index_map', 'delta_ns': None} for name in sources}
+    arrays = {k: np.stack(v) for k, v in arrays.items()}
+    xyz = np.full((count, 2, 21, 3), np.nan, np.float32)
+    xyz[:, 1] = arrays['xyz']
+    valid = np.zeros((count, 2, 21), bool)
+    valid[:, 1] = True
+    uv = np.full((count, 2, 21, 2), np.nan, np.float32)
+    homogeneous = np.einsum('tij,tkj->tki', arrays['intrinsic'], arrays['xyz'])
+    projectable = homogeneous[:, :, 2] > 0
+    uv[:, 1][projectable] = (homogeneous[:, :, :2][projectable] / homogeneous[:, :, 2:][projectable])
+    uv_valid = valid.copy()
+    uv_valid[:, 1] &= projectable
+    common_source = {'file': 'camera/frames.jsonl', 'fields': ['annotation_sources'], 'records': 'all frame_index rows'}
+    policy = {'rule': 'source present AND finite/numerical checks; no additional official QA input',
+              'scope': 'per joint for keypoints; per frame for camera/pose',
+              'official_split_is_not_visibility': True, 'local_excluded_frame_indices': []}
+    specs = [('intrinsic', 'camera/pinhole/v1', 'camera/intrinsic.pt', arrays['intrinsic'], np.ones(count, bool), ['T', 3, 3]),
+             ('extrinsic', 'transform/se3/v1', 'camera/extrinsic.pt', arrays['extrinsic'], np.ones(count, bool), ['T', 4, 4]),
+             ('keypoints_xyz', 'keypoints/xyz/v1', 'annotation/keypoints/xyz.pt', xyz, valid, ['T', 'hand', 'keypoint', 'xyz']),
+             ('keypoints_uv', 'keypoints/uv/v1', 'annotation/keypoints/uv.pt', uv, uv_valid, ['T', 'hand', 'keypoint', 'uv']),
+             ('object_pose', 'object/rigid/v1', f'annotation/object/{objid}/pose.pt', arrays['pose'], np.ones(count, bool), ['T', 4, 4])]
+    for name, schema, ref, data, ok, axes in specs:
+        vref = ref.replace('.pt', '_valid.pt')
+        tensor(view, ref, data)
+        tensor(view, vref, ok)
+        extra = {'valid_policy': policy}
+        if name.startswith('keypoints'):
+            extra.update(keypoint_schema='mano21/v1', coordinate_frame='camera',
+                         unit='pixel' if name.endswith('uv') else 'm')
+        if name in ('extrinsic', 'object_pose'):
+            extra.update(unit='m', convention='T_camera_from_world' if name == 'extrinsic' else 'T_camera_from_object')
+        meta['components'][name] = component(schema, ref, axes, vref, ['T', 'hand', 'keypoint'] if ok.ndim == 3 else ['T'],
+            common_source, 'K @ official camera-space hand_j; positive Z only' if name.endswith('uv') else 'official per-sample array, float32; right-hand slot 1',
+            kind='derived' if name.endswith('uv') else 'format_conversion', **extra)
+    oak_object_asset(view, rows, meta, root, index)
+    meta['camera'].update(model='pinhole', schema_id='camera/pinhole/v1')
+    meta.update(split_scheme=split_scheme, split_source_locator={'file': 'camera/frames.jsonl', 'field': 'split_source_locator'})
+
+
+def build_view(out, seq, cam, size, rows, args, index):
+    view = out / 'data' / seq / cam
+    view.mkdir(parents=True)
+    frames = []
+    for i, r in enumerate(rows):
+        src = locator(r['image'], args.input, index)
+        frames.append({'frame_index': i, 'sample_id': r['sample_id'], 'source_frame_id': r.get('source_frame_id', r['image'].name),
+            'timestamp_ns': None, 'clock_domain': None, 'source_locator': src,
+            'split': r['split'], 'source_split': r['source_split']})
+        if 'mask' in r:
+            frames[-1]['mask_source_locator'] = locator(r['mask'], args.input, index)
+    source_ref = {'file': 'camera/frames.jsonl', 'field': 'source_locator', 'records': 'all frame_index rows'}
+    count = len(rows)
+    storage, proof = encode([r['image'] for r in rows], view / 'observation/rgb.mkv')
+    tensor(view, 'observation/rgb_valid.pt', np.ones(count, bool))
+    comps = {'rgb': component('rgb/uint8/v1', 'observation/rgb.mkv', ['T', 'height', 'width', 'RGB'],
+        'observation/rgb_valid.pt', ['T'], source_ref, 'exact source JPEG remux or exact RGB round-trip re-encoding', storage=storage)}
+    ego = args.dataset == 'EgoHOS'
+    meta = {'schema_version': 'hand-warehouse/1.0', 'dataset_id': args.dataset, 'release_id': 'shared-v1.0',
+        'sequence_id': seq, 'camera_id': cam, 'sample_type': 'image' if ego else 'video',
+        'num_frames': count, 'hand_slots': ['left', 'right'], 'view_type': 'ego' if ego else 'exo',
+        'split': rows[0]['split'] if len({r['split'] for r in rows}) == 1 else 'unknown',
+        'sampling': {'type': ('single' if count == 1 else 'independent') if ego else 'sparse',
+            'source_fps': None, 'frames_ref': 'camera/frames.jsonl', 'strategy': args.phase,
+            'seed': args.seed, 'timestamps': 'unknown; no timestamps inferred from filenames or MKV PTS'},
+        'camera': {'model': 'unknown', 'schema_id': None, 'image_size': list(size)}, 'components': comps}
+    proofs = {'rgb': proof}
+    missing = {'depth': 'not provided by selected source image release',
+        'mano': 'not exported: model profile/flat-hand-mean/translation conversion not empirically verified',
+        'visibility': 'no independent source visibility input; never inferred from valid',
+        'contact': 'no verified per-entity or vertex contact annotations in selected input'}
+    if ego:
+        meta.update(split_scheme='EgoHOS-official-four-directories', split_source_locator=source_ref)
+        storage, proofs['segment'] = encode([r['mask'] for r in rows], view / 'annotation/segment/mask.mkv', labels=True)
+        tensor(view, 'annotation/segment/mask_valid.pt', np.ones(count, bool))
+        labels = [{'mask_value': 9 if i == 0 else i, 'category_id': name,
+                   'entity_id': 'hand_left' if i == 1 else 'hand_right' if i == 2 else None,
+                   'part_id': None, 'source_instance_id': None, 'source_attributes': {'source_class_id': i}}
+                  for i, name in enumerate(LABELS)]
+        jsonl(view / 'annotation/segment/instances.jsonl', [{'frame_index': i, 'labels': labels} for i in range(count)])
+        comps['segment'] = component('segment/uint16_labels/v1', 'annotation/segment/mask.mkv', ['T', 'height', 'width'],
+            'annotation/segment/mask_valid.pt', ['T'], {'file': 'camera/frames.jsonl', 'field': 'mask_source_locator'},
+            'source 0 background -> 9; 1..8 unchanged; exact uint16 round-trip',
+            storage=storage, segmentation_type='semantic', mapping_ref='annotation/segment/instances.jsonl',
+            coverage={'kind': 'partial', 'meaning': 'hands and selected interacting object categories; background is source task background, not exhaustive object annotation'},
+            valid_policy={'rule': 'paired source label exists, shape and integer categories verified; no additional official QA supplied', 'local_excluded_frame_indices': []})
+        missing.update(intrinsic='source calibration absent', extrinsic='source calibration absent',
+                       keypoints='source keypoints absent', object='semantic classes do not establish instance identity/mesh/pose',
+                       contact='raw package lacks contact/; interaction-order categories are not per-entity contact labels')
+    elif args.annotations:
+        oak_geometry(view, rows, frames, meta, args.input, index, args.annotations, args.split_scheme)
+    else:
+        missing.update(intrinsic='anno_v2_1.zip missing', extrinsic='anno_v2_1.zip missing',
+            keypoints='anno_v2_1.zip missing', object_pose='anno_v2_1.zip missing; canonical mesh does not establish camera-space pose')
+        if args.object_assets_present:
+            oak_object_asset(view, rows, meta, args.input, index)
+        else:
+            missing['object_mesh'] = 'image/obj.zip exists upstream but has not been downloaded/extracted into this input'
+        meta['split_reason'] = 'official split files are in missing anno_v2_1.zip; unknown is not a new split'
+    for name, reason in missing.items():
+        comps[name] = {'available': False, 'reason': reason}
+    jsonl(view / 'camera/frames.jsonl', frames)
+    dump(view / 'metainfo.json', meta)
+    dump(view / 'encoding_validation.json', proofs)
+    return view.relative_to(out).as_posix()
+
+
+def recipe(args):
+    names = ['preprocess.py', 'media.py', 'validate.py', 'acquire.py', 'schema.json', 'CONTRACT.md']
+    return {'dataset': args.dataset, 'chunk': args.chunk, 'seed': args.seed,
+            'annotations': bool(args.annotations), 'split_scheme': args.split_scheme,
+            'code_sha256': {n: sha(HERE / n) for n in names}}
+
+
+def approve(root, reviewer):
+    from validate import check_inventory
+    report = root / 'doc/validation/report.json'
+    d = load(report)
+    check_inventory(root, d['file_hashes'])
+    if not reviewer.strip() or not d['automatic_validation_passed']:
+        raise ValueError('Reviewer and passing automatic checks required')
+    # A human runs this only AFTER viewing the HTML. It is a record, not automatic approval.
+    dump(root / 'doc/validation/human_review.json', {'reviewer': reviewer, 'phase': d['phase'],
+        'report_sha256': sha(report), 'recipe': d['recipe'], 'approved': True,
+        'statement': 'I inspected the listed HTML sample IDs and accept the documented scope and limitations.'})
+
+
+def require_review(root):
+    from validate import check_inventory
+    report = root / 'doc/validation/report.json'
+    reviewed = load(root / 'doc/validation/human_review.json')
+    if not reviewed.get('approved') or reviewed['report_sha256'] != sha(report):
+        raise ValueError('Missing/stale human review; inspect HTML and run approve')
+    result = load(report)
+    check_inventory(root, result['file_hashes'])
+    return result
+
+
+def run(args):
+    if args.dataset == 'OakInk-v1' and not args.annotations and not args.allow_rgb_only:
+        raise ValueError('OakInk annotations absent. Supply --annotations + --trust-pickle, or explicitly select --allow-rgb-only (limited scope).')
+    if args.annotations and (not args.trust_pickle or not args.annotations.resolve().is_relative_to(args.input.resolve())):
+        raise ValueError('--annotations must be under --input and requires --trust-pickle; pickle can execute code')
+    if args.out.exists():
+        raise ValueError('Output exists; choose a fresh directory. Incomplete output is never reused as a release.')
+    expected = recipe(args)
+    if args.phase == 'full':
+        if not args.pilot:
+            raise ValueError('Full processing requires --pilot with a human review')
+        reviewed = require_review(args.pilot)
+        if reviewed['phase'] != 'pilot' or reviewed['recipe'] != expected:
+            raise ValueError('Pilot recipe does not match; re-run and review pilot after code/config changes')
+        acquisition = args.input / 'acquisition.json'
+        if not acquisition.exists() or load(acquisition).get('command') != 'extract' or load(acquisition).get('dataset') != args.dataset:
+            raise ValueError('Full processing requires complete, verified acquire.py extract input, not a partial pilot download')
+    index = source_index(args.input)
+    args.object_assets_present = any(p.is_dir() for p in args.input.rglob('obj')) if args.dataset == 'OakInk-v1' else False
+    if args.phase == 'full':
+        for pf in (args.pilot / 'data').glob('*/*/camera/frames.jsonl'):
+            for line in pf.read_text().splitlines():
+                f = json.loads(line)
+                for field in ('source_locator', 'mask_source_locator'):
+                    if field in f:
+                        old = f[field]
+                        current = index.get(old['extracted_path'])
+                        if current is None or current['sha256'] != old['extracted_sha256']:
+                            raise ValueError('Full input differs from reviewed pilot source samples')
+    rows = inventory_ego(args.input) if args.dataset == 'EgoHOS' else inventory_oak(args.input, args.annotations, args.split_scheme)
+    if args.phase == 'full' and args.dataset == 'EgoHOS':
+        expected_counts = dict(train=8993, val=1124, test_indomain=1126, test_outdomain=500)
+        actual_counts = {s: sum(r['source_split'] == s for r in rows) for s in expected_counts}
+        if actual_counts != expected_counts:
+            raise ValueError(f'Incomplete/wrong EgoHOS full inventory: {actual_counts}; expected {expected_counts}')
+    ids = [r['sample_id'] for r in rows]
+    if len(set(ids)) != len(ids):
+        raise ValueError('Duplicate sample IDs')
+    selected = select_rows(rows, args.dataset, args.phase, args.per_stratum, args.seed)
+    plan = groups(selected, args.dataset, args.chunk)
+    raw_bytes = sum(r['image'].stat().st_size for r in selected)
+    # Conservative preflight, then check again before each view; no claim of exact compression ratio.
+    estimated = max(raw_bytes * 4, max(w*h*len(rs)*8 for _, _, (w, h), rs in plan)) + 1024**3
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    if shutil.disk_usage(args.out.parent).free < estimated:
+        raise ValueError(f'Estimated working/output space {estimated/1e9:.1f} GB exceeds free space')
+    args.out.mkdir()
+    (args.out / 'script').mkdir()
+    (args.out / 'doc').mkdir()
+    for p in HERE.glob('*.py'):
+        shutil.copy2(p, args.out / 'script' / p.name)
+    for name in ['schema.json', 'CONTRACT.md', 'requirements.txt', 'README.md',
+                 'EgoHOS_DATA_MAPPING.md', 'OakInk-v1_DATA_MAPPING.md']:
+        shutil.copy2(HERE / name, args.out / 'script' / name)
+    shutil.copytree(HERE / 'source_evidence', args.out / 'script/source_evidence')
+    for name in ['schema.json', 'CONTRACT.md']:
+        shutil.copy2(HERE / name, args.out / 'doc' / name)
+    mapping = HERE / f'{args.dataset}_DATA_MAPPING.md'
+    for name in ['DATA_MAPPING.md', 'RAW_TO_SHARED.md']:
+        shutil.copy2(mapping, args.out / 'doc' / name)
+    shutil.copy2(HERE / 'README.md', args.out / 'doc/RUNBOOK.md')
+    views = []
+    for i, (seq, cam, size, batch) in enumerate(plan):
+        if shutil.disk_usage(args.out).free < size[0]*size[1]*len(batch)*8 + 512*1024**2:
+            raise RuntimeError('Low disk; stopped before next view, output remains incomplete')
+        views.append(build_view(args.out, seq, cam, size, batch, args, index))
+        print(f'{i+1}/{len(plan)} {seq}/{cam} T={len(batch)}', flush=True)
+    manifest = {'dataset': args.dataset, 'phase': args.phase, 'recipe': expected,
+        'inventory_samples': len(rows), 'selected_samples': len(selected), 'views': views,
+        'input_sources_sha256': sha(args.input / 'sources.jsonl'),
+        'sample_selection': {'seed': args.seed, 'per_stratum': args.per_stratum, 'pilot_strata': 'official source split or split x camera'},
+        'source_inventory': {'sample_ids_sha256': hashlib.sha256('\n'.join(sorted(ids)).encode()).hexdigest()},
+        'human_status': 'pending', 'command': sys.argv,
+        'limitations': ['OakInk MANO conversion not implemented until model-profile and translation parity verified',
+                       'OakInk-Shape/OakBase excluded: object-space grasp/knowledge assets are not synchronized camera observations',
+                       'No source visibility/contact guessed from geometry or masks']}
+    if args.phase == 'full':
+        shutil.copy2(args.pilot / 'doc/validation/human_review.json', args.out / 'doc/pilot_human_review.json')
+    dump(args.out / 'doc/build.json', manifest)
+    from validate import validate_release
+    validate_release(args.out, args.per_stratum, args.seed, source_root=args.input)
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    sub = p.add_subparsers(dest='command', required=True)
+    b = sub.add_parser('build')
+    b.add_argument('--dataset', choices=['EgoHOS', 'OakInk-v1'], required=True)
+    b.add_argument('--input', type=Path, required=True)
+    b.add_argument('--out', type=Path, required=True)
+    b.add_argument('--phase', choices=['pilot', 'full'], default='pilot')
+    b.add_argument('--pilot', type=Path)
+    b.add_argument('--chunk', type=int, default=128)
+    b.add_argument('--per-stratum', type=int, default=4)
+    b.add_argument('--seed', type=int, default=20260928)
+    b.add_argument('--annotations', type=Path)
+    b.add_argument('--split-scheme', choices=['split0', 'split1', 'split2', 'split0_ho'], default='split0')
+    b.add_argument('--trust-pickle', action='store_true')
+    b.add_argument('--allow-rgb-only', action='store_true')
+    a = sub.add_parser('approve', help='Human review record; never run automatically')
+    a.add_argument('--release', type=Path, required=True)
+    a.add_argument('--reviewer', required=True)
+    u = sub.add_parser('upload', help='Upload reviewed FULL release to the requested raw repository')
+    u.add_argument('--release', type=Path, required=True)
+    args = p.parse_args()
+    if args.command == 'build':
+        if args.chunk < 1 or args.per_stratum < 1:
+            p.error('chunk and per-stratum must be positive')
+        run(args)
+    elif args.command == 'approve':
+        approve(args.release, args.reviewer)
+    else:
+        from validate import check_inventory
+        d = require_review(args.release)
+        if d['phase'] != 'full':
+            raise ValueError('Pilot releases cannot be uploaded as shared-v1.0')
+        check_inventory(args.release, d['file_hashes'])
+        from huggingface_hub import HfApi
+        target = f'{d["dataset"]}/shared-v1.0'
+        api = HfApi()
+        # Refuse to merge into an existing release with stale files.
+        existing = api.list_repo_files('ParityLab/Hand-Datasets-Raw', repo_type='dataset')
+        if any(x.startswith(target + '/') for x in existing):
+            raise ValueError(f'Remote {target} already exists; publish a new version or review replacement separately')
+        from huggingface_hub import CommitOperationAdd
+        files = sorted((p for p in args.release.rglob('*') if p.is_file() and '__pycache__' not in p.parts),
+                       key=lambda p: (p.relative_to(args.release).parts[0] == 'doc', p.as_posix()))
+        # Bounded commits avoid a single enormous commit; publish build/review documents last.
+        for start in range(0, len(files), 128):
+            operations = [CommitOperationAdd(path_in_repo=target + '/' + f.relative_to(args.release).as_posix(),
+                                              path_or_fileobj=f) for f in files[start:start+128]]
+            result = api.create_commit(repo_id='ParityLab/Hand-Datasets-Raw', repo_type='dataset',
+                operations=operations, commit_message=f'Add reviewed {target} files {start+1}-{min(start+128, len(files))}')
+            print(result, flush=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/EgoHOS/shared-v1.0/script/requirements.txt b/EgoHOS/shared-v1.0/script/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b5857677355ac2fa0c9796e63d4cface6fe445f
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/requirements.txt
@@ -0,0 +1,5 @@
+numpy>=1.26
+Pillow>=10
+torch>=2.2
+av>=14
+huggingface_hub>=0.27
diff --git a/EgoHOS/shared-v1.0/script/resample_validation.py b/EgoHOS/shared-v1.0/script/resample_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..acf5c9bfa6c5b5d7842197cd026f6da5fadac17a
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/resample_validation.py
@@ -0,0 +1,294 @@
+#!/usr/bin/env python3
+"""Validate all shared frames; render capped samples of every available modality."""
+import argparse
+from collections import defaultdict
+import hashlib
+import html
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from acquire import sha
+from media import decode, rgb
+
+COLORS = [(0, 0, 0), (255, 70, 80), (50, 150, 255), (255, 180, 40), (80, 220, 100),
+          (220, 80, 220), (140, 90, 40), (40, 220, 220), (150, 150, 255), (70, 70, 70)]
+EDGES = [(0, i) for i in (1, 5, 9, 13, 17)] + [(i, i+1) for i in range(1, 21) if i % 4 != 0]
+
+
+def read_json(path):
+    return json.loads(path.read_text())
+
+
+def read_jsonl(path):
+    return [json.loads(line) for line in path.read_text().splitlines()]
+
+
+def pt(path):
+    t = torch.load(path, map_location='cpu', weights_only=True)
+    if not isinstance(t, torch.Tensor) or t.device.type != 'cpu':
+        raise ValueError(f'Expected CPU Tensor: {path}')
+    return t.numpy()
+
+
+def local_ref(view, ref):
+    p = view / ref
+    if not p.resolve().is_relative_to(view.resolve()) or not p.is_file():
+        raise ValueError(f'Broken/unsafe reference: {ref}')
+    return p
+
+
+def check_inventory(root, expected):
+    actual = {p.relative_to(root).as_posix(): sha(p) for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts
+              and p.relative_to(root).as_posix() not in ('doc/validation/report.json', 'doc/validation/human_review.json')}
+    if actual != expected:
+        raise ValueError('Release contents changed after validation; revalidate and repeat human review')
+
+
+def validate_view(view, registry):
+    m = read_json(view / 'metainfo.json')
+    frames = read_jsonl(view / 'camera/frames.jsonl')
+    t = m['num_frames']
+    if t < 1 or m['hand_slots'] != ['left', 'right'] or len(frames) != t:
+        raise ValueError('Frame count/hand-slot error')
+    if m['view_type'] not in ('ego', 'exo', 'unknown') or m['schema_version'] != registry['schema_version']:
+        raise ValueError('Invalid schema/view_type')
+    if [r['frame_index'] for r in frames] != list(range(t)) or len({r['sample_id'] for r in frames}) != t:
+        raise ValueError('Invalid/duplicate frame IDs')
+    for f in frames:
+        for key in ['timestamp_ns', 'clock_domain', 'source_locator', 'source_frame_id']:
+            if key not in f:
+                raise ValueError(f'Missing frame field {key}')
+    proofs = read_json(view / 'encoding_validation.json')
+    for name, c in m['components'].items():
+        if not c['available']:
+            if not c.get('reason'):
+                raise ValueError('Missing unavailable reason')
+            continue
+        schema = registry['components'][c['schema_id']]
+        data = local_ref(view, c['data_ref'])
+        valid = pt(local_ref(view, c['valid_ref'])) if 'valid_ref' in c else None
+        if valid is not None and valid.dtype != bool:
+            raise ValueError('Validity must be bool')
+        if 'valid_shape' in schema and valid is not None:
+            shape = tuple(t if x == 'T' else x for x in schema['valid_shape'])
+            if valid.shape != shape:
+                raise ValueError(f'{name} valid shape {valid.shape} != {shape}')
+        if data.suffix == '.pt':
+            a = pt(data)
+            shape = tuple(t if x == 'T' else x for x in schema['shape'])
+            if a.shape != shape or a.dtype != np.dtype(schema['dtype']):
+                raise ValueError(f'{name} tensor shape/dtype mismatch')
+            ok = np.broadcast_to(valid.reshape(valid.shape + (1,)*(a.ndim-valid.ndim)), a.shape)
+            if not np.isfinite(a[ok]).all() or not np.isnan(a[~ok]).all():
+                raise ValueError(f'{name}: finite valid / NaN invalid policy failed')
+            if c['schema_id'] in ('object/rigid/v1', 'transform/se3/v1'):
+                r = a[valid, :3, :3]
+                if not (np.allclose(np.swapaxes(r, 1, 2) @ r, np.eye(3), atol=1e-4) and np.allclose(np.linalg.det(r), 1, atol=1e-4)):
+                    raise ValueError('SE3 check failed')
+        elif data.suffix == '.mkv':
+            storage = c['storage']
+            if storage['profile'] not in schema['allowed_storage_profiles']:
+                raise ValueError('Unregistered storage profile')
+            for required in registry['video_metadata']['required']:
+                if required not in storage:
+                    raise ValueError(f'Missing storage field {required}')
+            chosen = next(x for x in proofs[name]['candidates'] if x['encoder'] == proofs[name]['selected'])
+            hashes = []
+            mapping = read_jsonl(local_ref(view, c['mapping_ref'])) if name == 'segment' else None
+            if mapping is not None and (len(mapping) != t or [x['frame_index'] for x in mapping] != list(range(t))):
+                raise ValueError('Segmentation mapping timeline differs')
+            for i, a in enumerate(decode(data, name == 'segment')):
+                if list(a.shape[1::-1]) != m['camera']['image_size']:
+                    raise ValueError('Video dimensions differ from camera')
+                hashes.append(hashlib.sha256(a.tobytes()).hexdigest())
+                if mapping is not None:
+                    codes = [x['mask_value'] for x in mapping[i]['labels']]
+                    if len(codes) != len(set(codes)) or not set(np.unique(a)).difference((0, 65535)).issubset(codes):
+                        raise ValueError('Invalid segment mapping')
+            if len(hashes) != t or hashes != chosen['ordered_decoded_sha256']:
+                raise ValueError('Video differs from exact source comparison proof')
+        elif data.suffix == '.obj':
+            if not any(line.startswith('v ') for line in data.read_text().splitlines()):
+                raise ValueError('Empty mesh')
+    return m, frames
+
+
+def clean_numbers(x):
+    if isinstance(x, np.generic):
+        return clean_numbers(x.item())
+    if isinstance(x, np.ndarray):
+        return clean_numbers(x.tolist())
+    if isinstance(x, list):
+        return [clean_numbers(v) for v in x]
+    if isinstance(x, float) and not np.isfinite(x):
+        return None
+    return x
+
+
+def preview_view(root, view, m, frames, target, source_root=None, selected_indices=None):
+    videos = {'rgb': decode(view / 'observation/rgb.mkv')}
+    c = m['components']
+    if c.get('segment', {}).get('available'):
+        videos['segment'] = decode(view / c['segment']['data_ref'], True)
+    arrays = {name: pt(view / component['data_ref']) for name, component in c.items()
+              if component['available'] and component.get('data_ref', '').endswith('.pt')}
+    instances = read_jsonl(view / c['segment']['mapping_ref']) if 'segment' in videos else None
+    objects = [read_json(p) for p in (view / 'annotation/object').glob('*/metainfo.json')]
+    cards = []
+    mesh = None
+    if c.get('object_mesh', {}).get('available'):
+        mesh = np.array([[float(v) for v in line.split()[1:4]] for line in (view / c['object_mesh']['data_ref']).read_text().splitlines() if line.startswith('v ')])
+        # Display-only point sampling; full OBJ asset is retained unchanged in data/.
+        mesh = mesh[::max(1, len(mesh)//3000)]
+    for i, frame in enumerate(frames):
+        rgb_array = next(videos['rgb'])
+        current_mask = next(videos['segment']) if 'segment' in videos else None
+        if selected_indices is not None and i not in selected_indices:
+            continue
+        name = hashlib.sha256(f'{view.relative_to(root)}/{i}'.encode()).hexdigest()[:20]
+        image = Image.fromarray(rgb_array)
+        overlay = image.copy()
+        if 'segment' in videos:
+            mask = current_mask
+            color = np.asarray(COLORS, dtype=np.uint8)[mask]
+            mix = rgb_array.copy()
+            foreground = (mask != 9) & (mask != 0)
+            mix[foreground] = (rgb_array[foreground] * .45 + color[foreground] * .55).astype(np.uint8)
+            overlay = Image.fromarray(mix)
+            Image.fromarray(color).save(target / f'{name}-mask.png')
+        draw = ImageDraw.Draw(overlay)
+        if 'keypoints_uv' in arrays:
+            pts = arrays['keypoints_uv'][i, 1]
+            for a, b in EDGES:
+                if np.isfinite(pts[[a, b]]).all():
+                    draw.line([tuple(pts[a]), tuple(pts[b])], fill=(0, 255, 100), width=2)
+            for k, xy in enumerate(pts):
+                if np.isfinite(xy).all():
+                    x, y = xy
+                    draw.ellipse((x-2, y-2, x+2, y+2), fill=(255, 50, 50))
+                    draw.text((x+3, y), str(k), fill=(255, 255, 255))
+        objcam = None
+        if mesh is not None and 'object_pose' in arrays:
+            pose = arrays['object_pose'][i]
+            objcam = mesh @ pose[:3, :3].T + pose[:3, 3]
+            projected = objcam @ arrays['intrinsic'][i].T
+            for x, y, z in projected[projected[:, 2] > 0]:
+                draw.point((float(x/z), float(y/z)), fill=(255, 210, 0))
+        image.save(target / f'{name}-rgb.png')
+        has_overlay = 'segment' in videos or 'keypoints_uv' in arrays or objcam is not None
+        if has_overlay:
+            overlay.save(target / f'{name}-overlay.png')
+        raw_ref = None
+        if source_root:
+            src = source_root / frame['source_locator']['extracted_path']
+            if sha(src) != frame['source_locator']['extracted_sha256']:
+                raise ValueError('Preview source differs from build source')
+            Image.fromarray(rgb(src)).save(target / f'{name}-source.png')
+            raw_ref = f'{name}-source.png'
+        values = {key: clean_numbers(a[i]) for key, a in arrays.items()}
+        valid = {key: clean_numbers(pt(view / item['valid_ref'])[i]) for key, item in c.items()
+                 if item['available'] and 'valid_ref' in item}
+        cards.append({'id': frame['sample_id'], 'view': view.relative_to(root).as_posix(),
+            'rgb': f'{name}-rgb.png', 'overlay': f'{name}-overlay.png' if has_overlay else None, 'source': raw_ref,
+            'mask': f'{name}-mask.png' if 'segment' in videos else None,
+            'frame': frame, 'arrays': values, 'valid': valid, 'metainfo': m,
+            'annotations': {'segment_instances': instances[i] if instances else None, 'object_metainfo': objects},
+            'object_points_camera': clean_numbers(objcam) if objcam is not None else None,
+            'object_points_canonical': clean_numbers(mesh) if mesh is not None else None})
+    return cards
+
+
+HTML = '''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><title>手物数据预处理检验</title>
+<style>body{font:16px system-ui;margin:24px;background:#101724;color:#ecf3ff}button,select,input{font:inherit;padding:7px}h1{font-size:25px}a{color:#7bd5ff}.grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px}img{width:100%;background:#000}pre{white-space:pre-wrap;overflow-wrap:anywhere;font:13px monospace;max-height:420px;overflow:auto}figure{margin:0}figcaption{padding:6px}canvas{background:#162434;max-width:100%;touch-action:none}.warning{padding:14px;background:#493915}details{padding:10px;background:#1c293c;margin:10px 0}label{display:block;margin:10px 0}</style>
+<h1>手物数据预处理检验</h1><p class="warning">待人工审核。自动无损校验通过 ≠ 标注语义或几何已获人工确认。只展示本报告列出的抽检样本；全量报告最多展示 192 帧。未提供模态及原因在元信息中明确列出。</p>
+<p>检查：源图与解码 RGB、左右手、标签颜色、源文件定位、valid、相机参数；有几何时检查 2D 投影和可旋转 3D 点云。绿色为手关键点，黄色为物体。3D 点云仅为显示抽样，完整模型见 data。</p>
+<label>样本 <select id="sample" aria-label="选择检验样本"></select></label><button id="prev">上一帧</button> <button id="next">下一帧</button><span id="counter"></span>
+<h2 id="title"></h2><div class="grid"><figure><img id="source" alt="源 RGB"><figcaption>源文件固定解码 RGB</figcaption></figure><figure><img id="rgb" alt="处理后 RGB"><figcaption>shared MKV 解码 RGB</figcaption></figure><figure><img id="overlay" alt="分割或几何叠加"><figcaption>分割 / 手关键点 / 物体叠加</figcaption></figure><figure><img id="mask" alt="语义标签图"><figcaption>标签图：1 左手红；2 右手蓝；3–8 交互物体；9 源背景灰</figcaption></figure></div>
+<section id="geometry"><p id="geometry-note"></p><div id="camera-geometry"><h2>相机系 3D（拖动旋转）</h2><canvas id="scene" width="700" height="460" aria-label="手物三维点云"></canvas></div><div id="canonical-geometry"><h2>物体规范坐标系 3D（拖动旋转）</h2><canvas id="canonical" width="700" height="460" aria-label="物体规范三维点云"></canvas></div></section>
+<details open><summary>逐帧来源、时间与 split</summary><pre id="frame"></pre></details><details><summary>所有数值标注与有效性</summary><pre id="numbers"></pre></details><details><summary>所有组件 / 缺失原因 / 相机 / 存储与映射</summary><pre id="meta"></pre></details>
+<script>const data=__DATA__;const $=x=>document.getElementById(x);let index=0,yaw=0,pitch=0;
+data.forEach((d,i)=>{let o=document.createElement('option');o.value=i;o.textContent=d.id;$('sample').append(o)});
+function cloud(canvas,points,colors){let ctx=canvas.getContext('2d');ctx.clearRect(0,0,canvas.width,canvas.height);if(!points.length)return;let center=[0,1,2].map(k=>points.reduce((s,p)=>s+p[k],0)/points.length);let q=points.map(p=>p.map((v,k)=>v-center[k]));let scale=180/Math.max(.00001,...q.flat().map(Math.abs));q.forEach((p,i)=>{let x=p[0]*Math.cos(yaw)+p[2]*Math.sin(yaw),z=-p[0]*Math.sin(yaw)+p[2]*Math.cos(yaw),y=p[1]*Math.cos(pitch)-z*Math.sin(pitch);ctx.fillStyle=colors[i];ctx.fillRect(350+x*scale,230+y*scale,3,3)})}
+function redraw(){let d=data[index],hand=(d.arrays.keypoints_xyz||[]).flat().filter(p=>p&&p.every(v=>v!==null)),obj=d.object_points_camera||[];cloud($('scene'),obj.concat(hand),obj.map(()=> '#ffd23f').concat(hand.map(()=> '#50ff9f')));let can=d.object_points_canonical||[];cloud($('canonical'),can,can.map(()=> '#ffd23f'))}
+function show(){let d=data[index];$('sample').value=index;$('title').textContent=d.id;$('counter').textContent=` ${index+1} / ${data.length}`;for(let k of ['source','rgb','overlay','mask']){let el=$(k);el.parentElement.hidden=!d[k];if(d[k])el.src=d[k];}$('frame').textContent=JSON.stringify(d.frame,null,2);$('numbers').textContent=JSON.stringify({arrays:d.arrays,valid:d.valid,annotations:d.annotations},null,2);$('meta').textContent=JSON.stringify(d.metainfo,null,2);$('geometry').hidden=!d.object_points_camera&&!d.object_points_canonical&&!d.arrays.keypoints_xyz;$('camera-geometry').hidden=!d.object_points_camera&&!d.arrays.keypoints_xyz;$('canonical-geometry').hidden=!d.object_points_canonical;$('geometry-note').textContent=d.object_points_canonical&&!d.object_points_camera?'仅有静态物体模型；缺少内参/位姿，不生成相机系叠加。':'';redraw()}
+$('sample').onchange=e=>{index=+e.target.value;show()};$('prev').onclick=()=>{index=(index+data.length-1)%data.length;show()};$('next').onclick=()=>{index=(index+1)%data.length;show()};for(let c of [$('scene'),$('canonical')]){let last=null;c.onpointerdown=e=>{last=[e.clientX,e.clientY];c.setPointerCapture(e.pointerId)};c.onpointerup=()=>last=null;c.onpointermove=e=>{if(last){yaw+=(e.clientX-last[0])*.01;pitch+=(e.clientY-last[1])*.01;last=[e.clientX,e.clientY];redraw()}}}show();</script></html>'''
+
+
+def sampled_indices(count, wanted, seed):
+    if wanted >= count:
+        return set(range(count))
+    fixed = {0, count - 1, count // 4, count // 2, 3 * count // 4}
+    rng = random.Random(seed)
+    remaining = [i for i in range(count) if i not in fixed]
+    fixed.update(rng.sample(remaining, wanted - len(fixed)))
+    return fixed
+
+
+def validate_release(root, per_stratum=4, seed=20260928, all_views=False, source_root=None,
+                     max_visual_frames=192):
+    build = read_json(root / 'doc/build.json')
+    registry = read_json(root / 'doc/schema.json')
+    results = {}
+    strata = defaultdict(list)
+    for relative in build['views']:
+        m, f = validate_view(root / relative, registry)
+        results[relative] = (m, f)
+        strata[(f[0]['source_split'], m['camera_id'])].append(relative)
+    rng = random.Random(seed)
+    chosen = sorted(results) if all_views or build['phase'] == 'pilot' else sorted(
+        v for s in sorted(strata) for v in rng.sample(strata[s], min(per_stratum, len(strata[s]))))
+    target = root / 'doc/validation'
+    target.mkdir(parents=True, exist_ok=True)
+    # Revalidation invalidates old human review, even if the numeric result stays the same.
+    (target / 'human_review.json').unlink(missing_ok=True)
+    for p in target.glob('*.png'):
+        p.unlink()
+    cards = []
+    allocation = {relative: None for relative in chosen}
+    if not all_views and build['phase'] == 'full' and max_visual_frames:
+        if max_visual_frames < len(chosen):
+            raise ValueError('max_visual_frames must cover every selected view')
+        base, extra = divmod(max_visual_frames, len(chosen))
+        for position, relative in enumerate(chosen):
+            count = len(results[relative][1])
+            wanted = min(count, base + (position < extra))
+            stable_seed = int.from_bytes(hashlib.sha256(f'{seed}/{relative}'.encode()).digest()[:8], 'big')
+            allocation[relative] = sampled_indices(count, wanted, stable_seed)
+    for relative in chosen:
+        m, f = results[relative]
+        cards.extend(preview_view(root, root / relative, m, f, target, source_root, allocation[relative]))
+    payload = json.dumps(cards, ensure_ascii=False, allow_nan=False).replace('<', '\\u003c')
+    (target / 'index.html').write_text(HTML.replace('__DATA__', payload))
+    hashes = {p.relative_to(root).as_posix(): sha(p) for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts
+              and p.relative_to(root).as_posix() not in ('doc/validation/report.json', 'doc/validation/human_review.json')}
+    report = {'dataset': build['dataset'], 'phase': build['phase'], 'recipe': build['recipe'],
+        'automatic_validation_passed': True, 'human_review': 'pending',
+        'all_views_checked': len(results), 'all_frames_checked': sum(m['num_frames'] for m, _ in results.values()),
+        'numeric_scope': 'every component, frame, shape, validity, registered schema and exact video decoded hash',
+        'visual_scope': 'all available modalities; deterministic frame samples from selected views',
+        'visual_selected_views': chosen, 'visual_sample_ids': [c['id'] for c in cards],
+        'visual_policy': {'maximum_frames': None if all_views else max_visual_frames,
+            'selection': 'per selected view: first, last, quartiles, then fixed-seed sample without replacement',
+            'frame_indices_by_view': {k: sorted(v) if v is not None else 'all' for k, v in allocation.items()}},
+        'seed': seed, 'file_hashes': hashes, 'limitations': build['limitations']}
+    (target / 'report.json').write_text(json.dumps(report, ensure_ascii=False, indent=2) + '\n')
+    print(f'Validated {report["all_frames_checked"]} frames; HTML: {target / "index.html"}', flush=True)
+    return report
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument('release', type=Path)
+    p.add_argument('--per-stratum', type=int, default=4)
+    p.add_argument('--seed', type=int, default=20260928)
+    p.add_argument('--all', action='store_true', help='Generate previews for every frame of every view; potentially large')
+    p.add_argument('--source-root', type=Path, help='Extracted input, to show source RGB alongside shared RGB')
+    p.add_argument('--max-visual-frames', type=int, default=192,
+                   help='Full-release visual sample cap; automatic validation still checks every frame')
+    a = p.parse_args()
+    validate_release(a.release, a.per_stratum, a.seed, a.all, a.source_root, a.max_visual_frames)
diff --git a/EgoHOS/shared-v1.0/script/schema.json b/EgoHOS/shared-v1.0/script/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..edb7cf7ce8e8e282d1fbfb5b39ef02a60321d2fb
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/schema.json
@@ -0,0 +1,880 @@
+{
+  "schema_version": "hand-warehouse/1.0",
+  "validity_revision": "usable-annotation-2026-09-20",
+  "annotation_validity": {
+    "meaning": "annotation usable for supervision, not merely record presence",
+    "formula": "source_annotation_present AND numerical_checks_pass AND applicable_official_QA_pass AND NOT explicitly_reviewed_local_exclusion",
+    "without_official_qa": "omit QA term; declare source rules; do not fabricate QA",
+    "policy_field": "components.<name>.valid_policy",
+    "visibility_is_independent": true,
+    "invalid_float_value": "NaN",
+    "hot3d": "broadcast per-camera frame manual QA conservatively to hand/object labels and pose-derived visibility; retain RGB and source calibration; keep native QA in raw and review report",
+    "hot3d_visibility": "native visibility_ratio[%] already ranges from 0 to 1; do not divide by 100"
+  },
+  "hand_slots": [
+    "left",
+    "right"
+  ],
+  "view_metadata": {
+    "required_fields": [
+      "view_type"
+    ],
+    "view_type_values": [
+      "ego",
+      "exo",
+      "unknown"
+    ],
+    "view_type_policy": "required per camera view; use ego or exo whenever the source data or capture setup establishes the viewpoint; use unknown only when no trustworthy source determines it; never omit or guess"
+  },
+  "keypoints": {
+    "mano21/v1": {
+      "count": 21,
+      "names": [
+        "wrist",
+        "thumb_1",
+        "thumb_2",
+        "thumb_3",
+        "thumb_4",
+        "index_1",
+        "index_2",
+        "index_3",
+        "index_4",
+        "middle_1",
+        "middle_2",
+        "middle_3",
+        "middle_4",
+        "ring_1",
+        "ring_2",
+        "ring_3",
+        "ring_4",
+        "little_1",
+        "little_2",
+        "little_3",
+        "little_4"
+      ],
+      "source_index_map": null,
+      "note": "adapter maps official MANO joint and fingertip convention"
+    },
+    "rhd21/v1": {
+      "count": 21,
+      "names": [
+        "wrist",
+        "thumb_1",
+        "thumb_2",
+        "thumb_3",
+        "thumb_4",
+        "index_1",
+        "index_2",
+        "index_3",
+        "index_4",
+        "middle_1",
+        "middle_2",
+        "middle_3",
+        "middle_4",
+        "ring_1",
+        "ring_2",
+        "ring_3",
+        "ring_4",
+        "little_1",
+        "little_2",
+        "little_3",
+        "little_4"
+      ],
+      "source_index_map_per_hand": [
+        0,
+        4,
+        3,
+        2,
+        1,
+        8,
+        7,
+        6,
+        5,
+        12,
+        11,
+        10,
+        9,
+        16,
+        15,
+        14,
+        13,
+        20,
+        19,
+        18,
+        17
+      ]
+    }
+  },
+  "camera": {
+    "opencv_fisheye4": {
+      "K": [
+        3,
+        3
+      ],
+      "distortion": [
+        "k1",
+        "k2",
+        "k3",
+        "k4"
+      ],
+      "projection": "theta=atan(r); theta_d=theta*(1+k1*theta^2+k2*theta^4+k3*theta^6+k4*theta^8); source ReInterHand"
+    },
+    "pinhole": {
+      "K": [
+        3,
+        3
+      ]
+    },
+    "fisheye624": {
+      "parameters": [
+        "f",
+        "cx",
+        "cy",
+        "k0",
+        "k1",
+        "k2",
+        "k3",
+        "k4",
+        "k5",
+        "p0",
+        "p1",
+        "s0",
+        "s1",
+        "s2",
+        "s3"
+      ]
+    },
+    "rational8": {
+      "K": [
+        3,
+        3
+      ],
+      "distortion": [
+        "k1",
+        "k2",
+        "p1",
+        "p2",
+        "k3",
+        "k4",
+        "k5",
+        "k6"
+      ]
+    }
+  },
+  "segmentation": {
+    "segment/uint16_labels/v1": {
+      "storage": "mask.mkv",
+      "dtype": "uint16",
+      "mapping": "instances.jsonl",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "reserved_codes": {
+        "0": "unassigned",
+        "65535": "ignore"
+      },
+      "region_codes": [
+        1,
+        65534
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    },
+    "segment/instance_polygons/v1": {
+      "storage": "polygons.jsonl",
+      "mapping": "instances.jsonl",
+      "frame_keys": [
+        "frame_index",
+        "instances"
+      ],
+      "instance_keys": [
+        "mask_value",
+        "polygons"
+      ],
+      "coordinate_unit": "pixel",
+      "overlap_allowed": true,
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref"
+      ],
+      "polygon_shape": [
+        "N",
+        2
+      ],
+      "holes_supported": false
+    },
+    "segment/foreground_intensity/v1": {
+      "storage": "mask.mkv",
+      "dtype": "uint16",
+      "value_range": [
+        0,
+        255
+      ],
+      "normalization_scale": 0.00392156862745098,
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+      "zero": "background",
+      "full_scale": "foreground",
+      "mapping": "foreground_category in component metadata",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "value_range",
+        "normalization_scale",
+        "foreground_category",
+        "entity_id",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    }
+  },
+  "note": "Registry for hand-warehouse/1.0. Read CONTRACT.md for field semantics. This is not a JSON Schema document or executable validator.",
+  "components": {
+    "mano/axis_angle61/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        2,
+        61
+      ],
+      "valid_shape": [
+        "T",
+        2
+      ],
+      "unit": "m",
+      "angle_unit": "rad",
+      "coordinate_frame": "camera",
+      "layout": {
+        "pose_axis_angle": [
+          0,
+          48
+        ],
+        "shape": [
+          48,
+          58
+        ],
+        "translation": [
+          58,
+          61
+        ]
+      }
+    },
+    "keypoints/uv/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        2,
+        21,
+        2
+      ],
+      "valid_shape": [
+        "T",
+        2,
+        21
+      ],
+      "unit": "pixel",
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "keypoints/xyz/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        2,
+        21,
+        3
+      ],
+      "valid_shape": [
+        "T",
+        2,
+        21
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "depth/z_float32/v1": {
+      "storage": "mkv",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "valid_shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "unit": "m",
+      "depth_type": "z",
+      "pixel_grid": "current_rgb",
+      "coordinate_frame": "current_rgb_camera",
+      "valid_storage": "mkv",
+      "allowed_storage_profiles": [
+        "mkv/depth_float32_bits/v1"
+      ],
+      "allowed_valid_storage_profiles": [
+        "mkv/bool_ffv1/v1"
+      ],
+      "required_metadata": [
+        "storage",
+        "valid_storage",
+        "depth_origin"
+      ],
+      "depth_origin_values": [
+        "measured",
+        "rendered",
+        "unknown"
+      ]
+    },
+    "transform/se3/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        4,
+        4
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "convention"
+      ]
+    },
+    "object/rigid/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        4,
+        4
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "convention"
+      ]
+    },
+    "camera/pinhole/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3,
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/rational8/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3,
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/opencv_fisheye4/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3,
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/fisheye624/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        15
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/distortion8/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        8
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "camera/fisheye4_distortion/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        4
+      ],
+      "valid_shape": [
+        "T"
+      ]
+    },
+    "object/joint_state/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "J"
+      ],
+      "valid_shape": [
+        "T",
+        "J"
+      ],
+      "required_metadata": [
+        "joint_order"
+      ],
+      "unit_rule": "revolute radians; prismatic metres; mixed types declared per joint"
+    },
+    "object_keypoints/camera/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "K",
+        3
+      ],
+      "valid_shape": [
+        "T",
+        "K"
+      ],
+      "unit": "m",
+      "coordinate_frame": "camera",
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "object/dimensions/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "dimensions_semantics"
+      ],
+      "axis_order": [
+        "length",
+        "width",
+        "height"
+      ],
+      "scale_rule": "do not apply to mesh without source-supported size/scale relation"
+    },
+    "object_keypoints/template/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "K",
+        3
+      ],
+      "valid_shape": [
+        "K"
+      ],
+      "unit": "m",
+      "coordinate_frame": "object",
+      "static": true,
+      "required_metadata": [
+        "keypoint_schema"
+      ]
+    },
+    "mesh/obj/v1": {
+      "storage": "obj",
+      "unit": "m",
+      "static": true,
+      "valid_shape": [],
+      "dependencies": "MTL and textures retained under owning object"
+    },
+    "rgb/uint8/v1": {
+      "storage": "mkv",
+      "dtype": "uint8",
+      "shape": [
+        "T",
+        "H",
+        "W",
+        3
+      ],
+      "valid_shape": [
+        "T"
+      ],
+      "color_order": "RGB",
+      "allowed_storage_profiles": [
+        "mkv/mjpeg_copy/v1",
+        "mkv/source_copy/v1",
+        "mkv/h264_rgb_lossless/v1",
+        "mkv/ffv1_rgb/v1"
+      ],
+      "required_metadata": [
+        "storage"
+      ]
+    },
+    "contact/relations/v1": {
+      "storage": "jsonl",
+      "valid_shape": [
+        "T"
+      ],
+      "frame_keys": [
+        "frame_index",
+        "relations"
+      ],
+      "absence_semantics": "no record is not a negative contact annotation"
+    },
+    "contact/vertex_region/v1": {
+      "storage": "pt",
+      "dtype": "bool",
+      "shape": [
+        "T",
+        "V"
+      ],
+      "valid_shape": [
+        "T",
+        "V"
+      ],
+      "required_metadata": [
+        "source_geometry_ref",
+        "target_geometry_ref",
+        "topology_id"
+      ],
+      "pilot_status": "defined_not_empirically_validated"
+    },
+    "contact/vertex_distance/v1": {
+      "storage": "pt",
+      "dtype": "float32",
+      "shape": [
+        "T",
+        "V"
+      ],
+      "valid_shape": [
+        "T",
+        "V"
+      ],
+      "unit": "m",
+      "required_metadata": [
+        "source_geometry_ref",
+        "target_geometry_ref",
+        "topology_id",
+        "distance_definition"
+      ],
+      "pilot_status": "defined_not_empirically_validated"
+    },
+    "object/articulated/v1": {
+      "storage": "json",
+      "required_keys": [
+        "parts",
+        "joints"
+      ],
+      "part_keys": [
+        "part_id",
+        "parent_id",
+        "has_model",
+        "mesh_ref"
+      ],
+      "hierarchy_semantics": "declare mechanical articulation or semantic containment; containment alone does not imply joints",
+      "acyclic": true
+    },
+    "segment/uint16_labels/v1": {
+      "storage": "mkv",
+      "dtype": "uint16",
+      "mapping": "instances.jsonl",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "reserved_codes": {
+        "0": "unassigned",
+        "65535": "ignore"
+      },
+      "region_codes": [
+        1,
+        65534
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    },
+    "segment/instance_polygons/v1": {
+      "storage": "jsonl",
+      "mapping": "instances.jsonl",
+      "frame_keys": [
+        "frame_index",
+        "instances"
+      ],
+      "instance_keys": [
+        "mask_value",
+        "polygons"
+      ],
+      "coordinate_unit": "pixel",
+      "overlap_allowed": true,
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "mapping_ref"
+      ],
+      "polygon_shape": [
+        "N",
+        2
+      ],
+      "holes_supported": false
+    },
+    "segment/foreground_intensity/v1": {
+      "storage": "mkv",
+      "dtype": "uint16",
+      "value_range": [
+        0,
+        255
+      ],
+      "normalization_scale": 0.00392156862745098,
+      "axes": [
+        "T",
+        "height",
+        "width"
+      ],
+      "meaning": "native hand foreground intensity; individual hand or union declared in metadata; not instance codes or calibrated probability",
+      "zero": "background",
+      "full_scale": "foreground",
+      "mapping": "foreground_category in component metadata",
+      "valid_shape": [
+        "T"
+      ],
+      "required_metadata": [
+        "segmentation_type",
+        "coverage",
+        "value_range",
+        "normalization_scale",
+        "foreground_category",
+        "entity_id",
+        "storage"
+      ],
+      "shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "allowed_storage_profiles": [
+        "mkv/uint16_ffv1/v1"
+      ]
+    }
+  },
+  "storage_revision": "lossless-video-2026-09-18",
+  "sampling": {
+    "image": {
+      "single": {
+        "T": 1
+      },
+      "independent": {
+        "T": ">=2",
+        "required_frame_fields": [
+          "sample_id",
+          "source_locator"
+        ],
+        "temporal_relationship": false
+      },
+      "source_fps": null
+    },
+    "video": {
+      "sampling_types": [
+        "dense",
+        "sparse"
+      ]
+    }
+  },
+  "storage_profiles": {
+    "mkv/mjpeg_copy/v1": {
+      "container": "matroska",
+      "codec": "mjpeg",
+      "method": "stream_copy_from_source_jpeg",
+      "logical_dtype": "uint8",
+      "decoded_pixel_format": "rgb24",
+      "validation": [
+        "source_jpeg_packet_sha256_exact",
+        "all_packet_count_order_and_dimensions_exact",
+        "deterministic_decoded_rgb_sample_exact",
+        "pilot_and_final_selected_whole_sequences_decoded_rgb_exact"
+      ],
+      "validation_policy": {
+        "id": "jpeg-packets-all-rgb-sample16/v1",
+        "sample_count_per_view": 16,
+        "short_views": "all_frames_if_T_less_than_16",
+        "fixed_indices": "0,T-1,T//4,T//2,3*T//4, deduplicated",
+        "remaining_indices": "sample without replacement to min(T,16) using Python random.Random(seed)",
+        "seed": "first 8 bytes of SHA256(sequence_id/camera_id/20260920), big-endian integer",
+        "report": "policy, exact selected frame indices, seed, separate packet and pixel coverage",
+        "legacy_all_frame_pixel_proofs": "still accepted; preserve historical evidence"
+      }
+    },
+    "mkv/source_copy/v1": {
+      "container": "matroska",
+      "codec": "source_codec_declared_in_metadata",
+      "method": "stream_copy_only",
+      "logical_dtype": "uint8",
+      "decoded_pixel_format": "rgb24",
+      "validation": [
+        "no_reencoding",
+        "all_decoded_rgb_frames_exact",
+        "presentation_frame_order_exact"
+      ]
+    },
+    "mkv/h264_rgb_lossless/v1": {
+      "container": "matroska",
+      "codec": "h264",
+      "encoder": "libx264rgb",
+      "crf": 0,
+      "decoded_pixel_format": "rgb24",
+      "logical_dtype": "uint8",
+      "gop": "declared_in_encoder_parameters",
+      "validation": [
+        "all_decoded_rgb_frames_exact"
+      ],
+      "forbidden": [
+        "lossy_rate_control",
+        "rgb_to_yuv420_subsampling"
+      ]
+    },
+    "mkv/ffv1_rgb/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "decoded_pixel_format": "rgb24",
+      "logical_dtype": "uint8",
+      "validation": [
+        "all_decoded_rgb_frames_exact"
+      ]
+    },
+    "mkv/depth_float32_bits/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "pixel_format": "gray16le",
+      "logical_dtype": "float32",
+      "logical_shape": [
+        "T",
+        "H",
+        "W"
+      ],
+      "encoded_shape": [
+        "T",
+        "H",
+        "2*W"
+      ],
+      "packing": "IEEE754 little-endian float32 bits; low uint16 in first W columns, high uint16 in last W columns",
+      "decode": "reinterpret_float32(uint32(low) | (uint32(high) << 16))",
+      "validation": [
+        "all_float32_bits_exact_including_nan"
+      ]
+    },
+    "mkv/bool_ffv1/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "pixel_format": "gray",
+      "logical_dtype": "bool",
+      "stored_values": [
+        0,
+        1
+      ],
+      "validation": [
+        "all_bool_values_exact"
+      ]
+    },
+    "mkv/uint16_ffv1/v1": {
+      "container": "matroska",
+      "codec": "ffv1",
+      "pixel_formats": [
+        "gray",
+        "gray16le"
+      ],
+      "logical_dtype": "uint16",
+      "gray_condition": "all values <=255; decode by exact uint8-to-uint16 promotion",
+      "validation": [
+        "all_integer_values_exact"
+      ]
+    }
+  },
+  "video_metadata": {
+    "required": [
+      "profile",
+      "codec",
+      "pixel_format",
+      "encoded_size",
+      "decoder",
+      "encoder_parameters",
+      "source_lossy"
+    ],
+    "source_lossy_values": [
+      true,
+      false,
+      "unknown"
+    ],
+    "timebase_role": "index_only",
+    "lossless_reference": "source compressed packets or fixed-decoder source pixels; depth uses normalized standard float32 bits",
+    "selection": "choose smaller complete candidate only after exact roundtrip validation"
+  }
+}
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-README.md b/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a007bd520a176142b09e8e7badda0997bf74858b
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-README.md
@@ -0,0 +1,34 @@
+# EgoHOS 数据集说明
+
+官方仓库说明在 `../script/README.md`。项目页：https://www.seas.upenn.edu/~shzhou2/projects/eos_dataset/ 。论文：Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications（ECCV 2022）。
+
+仓库 README 给出的解压布局是 `train` / `val` / `test_indomain` / `test_outdomain`，每组含 `image`、`label`、`contact`。本次保留的 zip 里没有 `contact/`，只有 `image/` 与 `label/`，计数见 `SOURCES.md`。
+
+`label` 的类别 id（摘自仓库 README）：
+
+```
+0 -> background
+1 -> left hand
+2 -> right hand
+3 -> 1st order interacting object by left hand
+4 -> 1st order interacting object by right hand
+5 -> 1st order interacting object by both hands
+6 -> 2nd order interacting object by left hand
+7 -> 2nd order interacting object by right hand
+8 -> 2nd order interacting object by both hands
+```
+
+README 另写明：contact 标注里值为 1 的像素是稠密接触区域。本包没有单独的 contact 图。
+
+引用：
+
+```
+@inproceedings{zhang2022fine,
+  title={Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications},
+  author={Zhang, Lingzhi and Zhou, Shenghao and Stent, Simon and Shi, Jianbo},
+  booktitle={European Conference on Computer Vision},
+  pages={127--145},
+  year={2022},
+  organization={Springer}
+}
+```
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-SOURCES.md b/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-SOURCES.md
new file mode 100644
index 0000000000000000000000000000000000000000..5ee91d31be37cd0c2a85a1af0ddb52e8aa918a5c
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-SOURCES.md
@@ -0,0 +1,21 @@
+# EgoHOS raw 来源
+
+官方仓库：https://github.com/owenzlz/EgoHOS ，本地 `../script/` 固定在提交 `cd9bdf427ef1d94582334ebba4188d5aa18433e7`（2023-11-02，`main`，与 `origin/main` 一致）。数据集由该提交中的 `download_datasets.sh` 下载：脚本用 `gdown` 拉取 Google Drive 文件 `1sk0TVEhZESNF67OW3fz9D5coqpIWkwuK`（链接为 `view?usp=sharing`），在 `script/data/` 解压后删除 zip。
+
+当前保留的是未再解压到 `raw/data/` 之外的外层包。`script/data/` 里只剩一次失败下载留下的 HTML：`view?usp=sharing`（80534 字节），不是数据集。
+
+拷贝后用 `archive_checksums.json` 对一下 MD5。没有单独的 `download_manifest.json`：这里不是多包下载计划，只有下面两个字节范围。
+
+- 外层包 `../data/egohos_dataset.zip`：4495628086 字节；MD5 `cbfbe909341d691880955f3d9d316404`；SHA256 `6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5`。`ZipFile.testzip()` 全部成员 CRC 通过。根目录只有 `data/`，其中同时放着官方 zip 和一份已解压副本。
+- 内嵌官方包 `data/egohos_dataset.zip`（存储、未再压缩）：2247683249 字节；MD5 `53e52744ce67fb54f8d5c34f0029a2e0`；SHA256 `a3fbfffa9c1506763041dff8001e4c56469b6a407e3fa6f2f694128651b1fc72`。该摘要由外层包内对应字节区间计算，不是 Google Drive 页面公布的摘要。
+
+内嵌包中央目录 23498 条，四个划分都只有 `image/` 与 `label/`，没有仓库 README 所写的 `contact/`：
+
+| 划分 | image | label |
+|---|---:|---:|
+| train | 8993 | 8993 |
+| val | 1124 | 1124 |
+| test_indomain | 1126 | 1126 |
+| test_outdomain | 500 | 500 |
+
+外层包里的解压副本与上表文件数相同，并多包一层 `data/` 前缀。
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-archive_checksums.json b/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-archive_checksums.json
new file mode 100644
index 0000000000000000000000000000000000000000..59654ec827406caefbe9cd72554dd639ef21a991
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/EgoHOS-archive_checksums.json
@@ -0,0 +1,18 @@
+{
+  "files": [
+    {
+      "path": "data/egohos_dataset.zip",
+      "size": 4495628086,
+      "md5": "cbfbe909341d691880955f3d9d316404",
+      "sha256": "6d2ba7eff16120dc6d42fb14e438406f421c381470d2fea88164e8fff1e570c5",
+      "zip_crc": "passed_all_members"
+    },
+    {
+      "path": "data/egohos_dataset.zip#data/egohos_dataset.zip",
+      "size": 2247683249,
+      "md5": "53e52744ce67fb54f8d5c34f0029a2e0",
+      "sha256": "a3fbfffa9c1506763041dff8001e4c56469b6a407e3fa6f2f694128651b1fc72",
+      "note": "外层包内未再压缩的官方 zip，按字节区间计算，不是单独落盘的文件"
+    }
+  ]
+}
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/OFFICIAL_CODE_REFERENCES.json b/EgoHOS/shared-v1.0/script/source_evidence/OFFICIAL_CODE_REFERENCES.json
new file mode 100644
index 0000000000000000000000000000000000000000..336db9a3a43c7d1a1c3b4ba96f6bd1297b9ceac0
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/OFFICIAL_CODE_REFERENCES.json
@@ -0,0 +1,14 @@
+{
+  "oikit/oi_image/oi_image.py": {
+    "url": "https://github.com/oakink/OakInk/blob/73dc7c85dfd006a4e6fc51fa8182d864a08af1ca/oikit/oi_image/oi_image.py",
+    "read_content_sha256": "ffdd211dcc1980b228d8905089da73d828f62c3fa41afef10fc3b843ba54278b"
+  },
+  "oikit/oi_image/utils.py": {
+    "url": "https://github.com/oakink/OakInk/blob/73dc7c85dfd006a4e6fc51fa8182d864a08af1ca/oikit/oi_image/utils.py",
+    "read_content_sha256": "36a6c81970b3c3c5f8881d7cdb523a2968e86c6b2e671d1e9444c2a27544fe83"
+  },
+  "docs/datasets.md": {
+    "url": "https://github.com/oakink/OakInk/blob/73dc7c85dfd006a4e6fc51fa8182d864a08af1ca/docs/datasets.md",
+    "read_content_sha256": "db6a26ae9c32d081fa4dbcca679680cf58617442bd0d7c124d22424167f31aad"
+  }
+}
\ No newline at end of file
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-README.md b/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d8f76a3eda395a25f2a58d6b9c6c99d5b6ce28d9
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-README.md
@@ -0,0 +1,5 @@
+# OakInk-v1
+
+Hugging Face：https://huggingface.co/datasets/oakink/OakInk-v1 。项目 https://oakink.net ；代码 https://github.com/oakink/OakInk ；论文 https://arxiv.org/abs/2203.15709 。许可（本地 `../data/README.md` / 数据集卡）：cc-by-nc-sa-3.0。
+
+下载范围与校验见 `SOURCES.md`、`archive_checksums.json`。
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-SOURCES.md b/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-SOURCES.md
new file mode 100644
index 0000000000000000000000000000000000000000..fd23f9e7bc35ee810453eaa5fb2c91e04cd35fb4
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-SOURCES.md
@@ -0,0 +1,7 @@
+# OakInk-v1 raw 来源
+
+Hugging Face 数据集页：https://huggingface.co/datasets/oakink/OakInk-v1 。本地由该仓库 `main` 树下载到 `data/`，未解压。页面标注总大小约 124 GB；许可 cc-by-nc-sa-3.0；论文 arXiv:2203.15709。本地 `data/README.md` 与页面一致，并给出项目 https://oakink.net 、代码 https://github.com/oakink/OakInk 。
+
+HF 树顶层为 `image/`、`shape/`、`OakBase.zip`（页面约 4.37 GB，本地 4366744375 字节）、`README.md`、`.gitattributes`。本地文件大小与 HF API 列出的 19 个文件中对应项一致；17 个压缩包均在本地。
+
+`archive_checksums.json` 对上述压缩包做 MD5/SHA256（含 `image/stream_zipped/oakink_image_v2.zip` 与 `.z01`–`.z10` 分卷）。未校验：`.cache/`、`.gitattributes`、`README.md`；未解压、未对包内帧逐文件哈希。路径相对 `raw/`（即 `data/…`）。
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-archive_checksums.json b/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-archive_checksums.json
new file mode 100644
index 0000000000000000000000000000000000000000..ef9abecc4613770073f83be9fca9f6fc165dfcf9
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/OakInk-v1-archive_checksums.json
@@ -0,0 +1,123 @@
+{
+  "files": [
+    {
+      "path": "data/OakBase.zip",
+      "size": 4366744375,
+      "md5": "5f9b08e65573b9419e8111ed8b9148f8",
+      "sha256": "58a0ea15a39e3de036d2c7abbcaa81f4b91e840867258b7e70bdf91c2013f756",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/OakBase.zip"
+    },
+    {
+      "path": "data/image/obj.zip",
+      "size": 7590433,
+      "md5": "7d34ccc1aceb818de712389952dafca5",
+      "sha256": "a519ab643a90e05eb7a5cf87c0cc0ceead8b9dfa630775607e79d87aae659c92",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/obj.zip"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.zip",
+      "size": 10640372368,
+      "md5": "ab8b611ddcae2a840b8a9a814d18c05c",
+      "sha256": "a18a5faece550a1116aa8071695922a9ef379f166fba78b9732c02c27a421a43",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.zip"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z01",
+      "size": 10737418240,
+      "md5": "0bb499e37f45e159400b2f617048e919",
+      "sha256": "b032eb41cfaf3013f05349b4680ff071208c46b064a5e2207e5a970943643f0b",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z01"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z02",
+      "size": 10737418240,
+      "md5": "a1e81c6d25ff62bcfe02dc87e9658239",
+      "sha256": "89643894c901544879833df36c96a6e97c63c913736a89ac6dc0396704ecf8f7",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z02"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z03",
+      "size": 10737418240,
+      "md5": "6c5ae2d07b73e48db172f7fa07896f42",
+      "sha256": "082e4d92a9965ee2ce42e8bcbe4323a12864e3d09ac1c47d92fb2f69ad1f5cd4",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z03"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z04",
+      "size": 10737418240,
+      "md5": "19da61d6b116e0f6b2a2d9e46a18d658",
+      "sha256": "824c3aac440fa859dc8e6083b59bb3d15e617ab53d5847243c3ae53247cba460",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z04"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z05",
+      "size": 10737418240,
+      "md5": "45a498e5632d338b034f31a144c7d9bd",
+      "sha256": "3e761772957ebb7b709167db9cfeb03294b0363981171eaafbac7b168939269e",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z05"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z06",
+      "size": 10737418240,
+      "md5": "7a23cfa0287b8fdcbaa9b49664bee0b4",
+      "sha256": "237d598eef4a7f77ad38522d4ec77d84d8377387868b5f3b9a2bbd111bb592f5",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z06"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z07",
+      "size": 10737418240,
+      "md5": "63fb1d424f942f4b5090e4e82adccd4a",
+      "sha256": "07d1ccc0abb151353ea709b3230b4729c076b35af09d24296efce814be6eb3fc",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z07"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z08",
+      "size": 10737418240,
+      "md5": "c898e8f05893222cb0b453010165a51a",
+      "sha256": "ce9ce3224d700808e6b036198cb3865aabce6d79b8bbb1ae9d25778302f5dac9",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z08"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z09",
+      "size": 10737418240,
+      "md5": "7cb09ec955c9ba3afc4e3a5cceb101ff",
+      "sha256": "9c66e195669b58af2c21dca0d00fc6a206e1febe76ae191cd8ba73e33f9265b0",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z09"
+    },
+    {
+      "path": "data/image/stream_zipped/oakink_image_v2.z10",
+      "size": 10737418240,
+      "md5": "df546a98817246a827d3914ae7beaef8",
+      "sha256": "44b149b4976abeef8a0a3e904b63e5c8b677b47004971e97768a7ff839216b47",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/image/stream_zipped/oakink_image_v2.z10"
+    },
+    {
+      "path": "data/shape/metaV2.zip",
+      "size": 15447,
+      "md5": "1a31292ab79ea4c9a0a4974a45ccaf07",
+      "sha256": "8c6ec8ebb98821c589170d98b73ccc84bf1543781b9ac3e4d1e6f01084e73bef",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/shape/metaV2.zip"
+    },
+    {
+      "path": "data/shape/oakink_shape_v2.zip",
+      "size": 48324871,
+      "md5": "4d1e81f47e3554d5bc2263700645bd4f",
+      "sha256": "d867060a5874acb73e1fcd132b014a2cfd024ef9ed171de53115c98db55c7d42",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/shape/oakink_shape_v2.zip"
+    },
+    {
+      "path": "data/shape/OakInkObjectsV2.zip",
+      "size": 1050467188,
+      "md5": "eb77599488589551878900d7abf08d59",
+      "sha256": "a969a5befdbccf6d0c5d30b42dfad3078c34e71788b54c644f660b192bcc9fd6",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/shape/OakInkObjectsV2.zip"
+    },
+    {
+      "path": "data/shape/OakInkVirtualObjectsV2.zip",
+      "size": 962778443,
+      "md5": "f447d892de4f5db716827cfd5c3bf3d0",
+      "sha256": "eb1a7fd45092571c8cd3509416913e461e8c3d56529e6e7e4bbf5258806dab8a",
+      "source_url": "https://huggingface.co/datasets/oakink/OakInk-v1/blob/main/shape/OakInkVirtualObjectsV2.zip"
+    }
+  ]
+}
diff --git a/EgoHOS/shared-v1.0/script/source_evidence/RAW_AVAILABILITY.json b/EgoHOS/shared-v1.0/script/source_evidence/RAW_AVAILABILITY.json
new file mode 100644
index 0000000000000000000000000000000000000000..12b403b37dab96fbbcf6d1816da140f0348f5edb
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/source_evidence/RAW_AVAILABILITY.json
@@ -0,0 +1,90 @@
+{
+  "revision": "8818d04d6cbdc0bf32b6ef949257b82310f16798",
+  "files": {
+    "EgoHOS": [
+      {
+        "path": "EgoHOS/raw/data/egohos_dataset.zip",
+        "size": 4495628086
+      }
+    ],
+    "OakInk-v1": [
+      {
+        "path": "OakInk-v1/raw/data/.gitattributes",
+        "size": 2350
+      },
+      {
+        "path": "OakInk-v1/raw/data/OakBase.zip",
+        "size": 4366744375
+      },
+      {
+        "path": "OakInk-v1/raw/data/README.md",
+        "size": 264
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/obj.zip",
+        "size": 7590433
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z01",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z02",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z03",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z04",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z05",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z06",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z07",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z08",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z09",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.z10",
+        "size": 10737418240
+      },
+      {
+        "path": "OakInk-v1/raw/data/image/stream_zipped/oakink_image_v2.zip",
+        "size": 10640372368
+      },
+      {
+        "path": "OakInk-v1/raw/data/shape/OakInkObjectsV2.zip",
+        "size": 1050467188
+      },
+      {
+        "path": "OakInk-v1/raw/data/shape/OakInkVirtualObjectsV2.zip",
+        "size": 962778443
+      },
+      {
+        "path": "OakInk-v1/raw/data/shape/metaV2.zip",
+        "size": 15447
+      },
+      {
+        "path": "OakInk-v1/raw/data/shape/oakink_shape_v2.zip",
+        "size": 48324871
+      }
+    ]
+  },
+  "oakink_image_annotations": "anno_v2_1.zip not present in raw tree"
+}
\ No newline at end of file
diff --git a/EgoHOS/shared-v1.0/script/test_self.py b/EgoHOS/shared-v1.0/script/test_self.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1dc868c836c67dfa5b0f14c04da3e40adea44bc
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/test_self.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""One small runnable end-to-end check; fixtures are synthetic, not real validation."""
+import json
+import pickle
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from acquire import sha, safe_path
+from media import mask, decode
+from preprocess import se3
+from validate import pt, check_inventory
+
+HERE = Path(__file__).resolve().parent
+
+
+def command(*args, succeeds=True):
+    p = subprocess.run([sys.executable, str(HERE / 'preprocess.py'), *map(str, args)], capture_output=True, text=True)
+    if (p.returncode == 0) != succeeds:
+        raise AssertionError(p.stdout + p.stderr)
+    return p
+
+
+def provenance(root):
+    entries = [{'path': p.relative_to(root).as_posix(), 'sha256': sha(p),
+                'source_locator': {'file': 'SYNTHETIC_TEST_FIXTURE', 'member': p.relative_to(root).as_posix()}}
+               for p in sorted(root.rglob('*')) if p.is_file() and p.name != 'sources.jsonl']
+    (root / 'sources.jsonl').write_text(''.join(json.dumps(r) + '\n' for r in entries))
+
+
+def save_pickle(path, value):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open('wb') as f:
+        pickle.dump(value, f)
+
+
+def main():
+    rng = np.random.default_rng(17)
+    with tempfile.TemporaryDirectory() as directory:
+        temp = Path(directory)
+        ego = temp / 'ego'
+        for split in ('train', 'val', 'test_indomain', 'test_outdomain'):
+            for folder in ('image', 'label'):
+                (ego / split / folder).mkdir(parents=True)
+            for i in range(2):
+                # One JPEG stratum and one PNG stratum exercise copy and both lossless encoders.
+                ext = '.jpg' if split != 'val' else '.png'
+                Image.fromarray(rng.integers(0, 256, (24, 32, 3), dtype=np.uint8)).save(ego / split / 'image' / f'{i}{ext}')
+                Image.fromarray(np.tile(np.arange(32, dtype=np.uint8) % 9, (24, 1))).save(ego / split / 'label' / f'{i}.png')
+        provenance(ego)
+        output = temp / 'ego-pilot'
+        command('build', '--dataset', 'EgoHOS', '--input', ego, '--out', output, '--chunk', 8)
+        report = json.loads((output / 'doc/validation/report.json').read_text())
+        assert report['all_frames_checked'] == 8
+        check_inventory(output, report['file_hashes'])
+        encoded = next((output / 'data').glob('train*/*/annotation/segment/mask.mkv'))
+        assert np.array_equal(next(decode(encoded, True)), mask(ego / 'train/label/0.png'))
+        assert (next(decode(encoded, True)) == 9).any()
+        # Missing human review must block full and upload before any remote call.
+        command('build', '--dataset', 'EgoHOS', '--input', ego, '--out', temp / 'badfull', '--phase', 'full', '--pilot', output, '--chunk', 8, succeeds=False)
+        command('upload', '--release', output, succeeds=False)
+        # A modified release fails its file inventory.
+        (output / 'data/unexpected.txt').write_text('modified')
+        try:
+            check_inventory(output, report['file_hashes'])
+        except ValueError:
+            pass
+        else:
+            raise AssertionError('Mutation accepted')
+
+        oak = temp / 'oak'
+        seq = 'A00001_0004_0001_0002/2021-01-01-00-00-00'
+        image = oak / 'image/stream_release_v2' / seq / 'north_east_color_10.png'
+        image.parent.mkdir(parents=True)
+        Image.fromarray(rng.integers(0, 256, (24, 32, 3), dtype=np.uint8)).save(image)
+        anno = oak / 'image/anno'
+        anno.mkdir()
+        info = [[seq, person, 10, 0] for person in (0, 1)]
+        (anno / 'seq_all.json').write_text(json.dumps(info))
+        for rel, value in [('split_train_val/split0/example_split_train.json', info),
+                           ('split_train_val/split0/example_split_val.json', []), ('split/split0/seq_test.json', [])]:
+            p = anno / rel
+            p.parent.mkdir(parents=True, exist_ok=True)
+            p.write_text(json.dumps(value))
+        k = np.array([[20, 0, 16], [0, 20, 12], [0, 0, 1]], np.float32)
+        xyz = np.column_stack((np.linspace(-.1, .1, 21), np.zeros(21), np.ones(21))).astype(np.float32)
+        pose = np.eye(4, dtype=np.float32)
+        pose[2, 3] = 1
+        for x in info:
+            key = '__'.join(map(str, x)).replace('/', '__') + '.pkl'
+            for folder, value in [('cam_intr', k), ('hand_j', xyz), ('obj_transf', pose), ('general_info', {'cam_extr': np.eye(4, dtype=np.float32)})]:
+                save_pickle(anno / folder / key, value)
+        obj = oak / 'image/obj/A00001.obj'
+        obj.parent.mkdir()
+        obj.write_text('mtllib absent.mtl\nv 0 0 0\nv .2 0 0\nv 0 .2 0\nusemtl default\nf 1 2 3\n')
+        (oak / 'acquisition.json').write_text(json.dumps({'command': 'extract', 'dataset': 'OakInk-v1', 'note': 'SYNTHETIC TEST ONLY'}))
+        provenance(oak)
+        command('build', '--dataset', 'OakInk-v1', '--input', oak, '--out', temp / 'oak-blocked', succeeds=False)
+        command('build', '--dataset', 'OakInk-v1', '--input', oak, '--out', temp / 'oak-rgb', '--allow-rgb-only')
+        out = temp / 'oak-geo'
+        command('build', '--dataset', 'OakInk-v1', '--input', oak, '--out', out,
+                '--annotations', anno, '--trust-pickle')
+        views = list((out / 'data').glob('*/*/metainfo.json'))
+        assert len(views) == 2 and any('person0' in str(p) for p in views) and any('person1' in str(p) for p in views)
+        for m in views:
+            v = m.parent
+            points = pt(v / 'annotation/keypoints/xyz.pt')
+            assert np.isnan(points[:, 0]).all() and np.array_equal(points[0, 1], xyz)
+            uv = pt(v / 'annotation/keypoints/uv.pt')
+            assert np.allclose(uv[0, 1, 0], [14, 12])
+            text = (v / 'annotation/object/A00001/assets/A00001.obj').read_text()
+            assert 'v -0.1 -0.1 0' in text
+            assert 'mtllib ' not in text and 'usemtl ' not in text
+            metadata = json.loads(m.read_text())
+            assert metadata['components']['object_mesh']['provenance']['source_missing_materials'] == ['absent.mtl']
+        # Test review records apply only to these temporary synthetic fixtures.
+        command('approve', '--release', out, '--reviewer', 'SYNTHETIC_TEST_ONLY')
+        command('upload', '--release', out, succeeds=False)  # Reviewed pilot still cannot publish.
+        command('build', '--dataset', 'OakInk-v1', '--input', oak, '--out', temp / 'oak-full',
+                '--annotations', anno, '--trust-pickle', '--phase', 'full', '--pilot', out)
+        for bad in ('../escape', '/absolute', 'a/../../escape'):
+            try:
+                safe_path(temp, bad)
+            except ValueError:
+                pass
+            else:
+                raise AssertionError('Unsafe path accepted')
+        bad = np.eye(4)
+        bad[0, 0] = 2
+        try:
+            se3(bad, 'bad')
+        except ValueError:
+            pass
+        else:
+            raise AssertionError('Invalid SE3 accepted')
+    print('PASS: JPEG copy, FFV1/x264 RGB, labels, provenance, review gates, RGB-only, synthetic OakInk geometry, NaN slots, projection, mesh centering, mutation/path/SE3 rejection')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/EgoHOS/shared-v1.0/script/validate.py b/EgoHOS/shared-v1.0/script/validate.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cfabd946196a102f833ce01e409b05e804adc5d
--- /dev/null
+++ b/EgoHOS/shared-v1.0/script/validate.py
@@ -0,0 +1,265 @@
+#!/usr/bin/env python3
+"""Validate all shared frames; render every available modality in sampled views."""
+import argparse
+from collections import defaultdict
+import hashlib
+import html
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageDraw
+import torch
+
+from acquire import sha
+from media import decode, rgb
+
+COLORS = [(0, 0, 0), (255, 70, 80), (50, 150, 255), (255, 180, 40), (80, 220, 100),
+          (220, 80, 220), (140, 90, 40), (40, 220, 220), (150, 150, 255), (70, 70, 70)]
+EDGES = [(0, i) for i in (1, 5, 9, 13, 17)] + [(i, i+1) for i in range(1, 21) if i % 4 != 0]
+
+
+def read_json(path):
+    return json.loads(path.read_text())
+
+
+def read_jsonl(path):
+    return [json.loads(line) for line in path.read_text().splitlines()]
+
+
+def pt(path):
+    t = torch.load(path, map_location='cpu', weights_only=True)
+    if not isinstance(t, torch.Tensor) or t.device.type != 'cpu':
+        raise ValueError(f'Expected CPU Tensor: {path}')
+    return t.numpy()
+
+
+def local_ref(view, ref):
+    p = view / ref
+    if not p.resolve().is_relative_to(view.resolve()) or not p.is_file():
+        raise ValueError(f'Broken/unsafe reference: {ref}')
+    return p
+
+
+def check_inventory(root, expected):
+    actual = {p.relative_to(root).as_posix(): sha(p) for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts
+              and p.relative_to(root).as_posix() not in ('doc/validation/report.json', 'doc/validation/human_review.json')}
+    if actual != expected:
+        raise ValueError('Release contents changed after validation; revalidate and repeat human review')
+
+
+def validate_view(view, registry):
+    m = read_json(view / 'metainfo.json')
+    frames = read_jsonl(view / 'camera/frames.jsonl')
+    t = m['num_frames']
+    if t < 1 or m['hand_slots'] != ['left', 'right'] or len(frames) != t:
+        raise ValueError('Frame count/hand-slot error')
+    if m['view_type'] not in ('ego', 'exo', 'unknown') or m['schema_version'] != registry['schema_version']:
+        raise ValueError('Invalid schema/view_type')
+    if [r['frame_index'] for r in frames] != list(range(t)) or len({r['sample_id'] for r in frames}) != t:
+        raise ValueError('Invalid/duplicate frame IDs')
+    for f in frames:
+        for key in ['timestamp_ns', 'clock_domain', 'source_locator', 'source_frame_id']:
+            if key not in f:
+                raise ValueError(f'Missing frame field {key}')
+    proofs = read_json(view / 'encoding_validation.json')
+    for name, c in m['components'].items():
+        if not c['available']:
+            if not c.get('reason'):
+                raise ValueError('Missing unavailable reason')
+            continue
+        schema = registry['components'][c['schema_id']]
+        data = local_ref(view, c['data_ref'])
+        valid = pt(local_ref(view, c['valid_ref'])) if 'valid_ref' in c else None
+        if valid is not None and valid.dtype != bool:
+            raise ValueError('Validity must be bool')
+        if 'valid_shape' in schema and valid is not None:
+            shape = tuple(t if x == 'T' else x for x in schema['valid_shape'])
+            if valid.shape != shape:
+                raise ValueError(f'{name} valid shape {valid.shape} != {shape}')
+        if data.suffix == '.pt':
+            a = pt(data)
+            shape = tuple(t if x == 'T' else x for x in schema['shape'])
+            if a.shape != shape or a.dtype != np.dtype(schema['dtype']):
+                raise ValueError(f'{name} tensor shape/dtype mismatch')
+            ok = np.broadcast_to(valid.reshape(valid.shape + (1,)*(a.ndim-valid.ndim)), a.shape)
+            if not np.isfinite(a[ok]).all() or not np.isnan(a[~ok]).all():
+                raise ValueError(f'{name}: finite valid / NaN invalid policy failed')
+            if c['schema_id'] in ('object/rigid/v1', 'transform/se3/v1'):
+                r = a[valid, :3, :3]
+                if not (np.allclose(np.swapaxes(r, 1, 2) @ r, np.eye(3), atol=1e-4) and np.allclose(np.linalg.det(r), 1, atol=1e-4)):
+                    raise ValueError('SE3 check failed')
+        elif data.suffix == '.mkv':
+            storage = c['storage']
+            if storage['profile'] not in schema['allowed_storage_profiles']:
+                raise ValueError('Unregistered storage profile')
+            for required in registry['video_metadata']['required']:
+                if required not in storage:
+                    raise ValueError(f'Missing storage field {required}')
+            chosen = next(x for x in proofs[name]['candidates'] if x['encoder'] == proofs[name]['selected'])
+            hashes = []
+            mapping = read_jsonl(local_ref(view, c['mapping_ref'])) if name == 'segment' else None
+            if mapping is not None and (len(mapping) != t or [x['frame_index'] for x in mapping] != list(range(t))):
+                raise ValueError('Segmentation mapping timeline differs')
+            for i, a in enumerate(decode(data, name == 'segment')):
+                if list(a.shape[1::-1]) != m['camera']['image_size']:
+                    raise ValueError('Video dimensions differ from camera')
+                hashes.append(hashlib.sha256(a.tobytes()).hexdigest())
+                if mapping is not None:
+                    codes = [x['mask_value'] for x in mapping[i]['labels']]
+                    if len(codes) != len(set(codes)) or not set(np.unique(a)).difference((0, 65535)).issubset(codes):
+                        raise ValueError('Invalid segment mapping')
+            if len(hashes) != t or hashes != chosen['ordered_decoded_sha256']:
+                raise ValueError('Video differs from exact source comparison proof')
+        elif data.suffix == '.obj':
+            if not any(line.startswith('v ') for line in data.read_text().splitlines()):
+                raise ValueError('Empty mesh')
+    return m, frames
+
+
+def clean_numbers(x):
+    if isinstance(x, np.generic):
+        return clean_numbers(x.item())
+    if isinstance(x, np.ndarray):
+        return clean_numbers(x.tolist())
+    if isinstance(x, list):
+        return [clean_numbers(v) for v in x]
+    if isinstance(x, float) and not np.isfinite(x):
+        return None
+    return x
+
+
+def preview_view(root, view, m, frames, target, source_root=None):
+    videos = {'rgb': decode(view / 'observation/rgb.mkv')}
+    c = m['components']
+    if c.get('segment', {}).get('available'):
+        videos['segment'] = decode(view / c['segment']['data_ref'], True)
+    arrays = {name: pt(view / component['data_ref']) for name, component in c.items()
+              if component['available'] and component.get('data_ref', '').endswith('.pt')}
+    instances = read_jsonl(view / c['segment']['mapping_ref']) if 'segment' in videos else None
+    objects = [read_json(p) for p in (view / 'annotation/object').glob('*/metainfo.json')]
+    cards = []
+    mesh = None
+    if c.get('object_mesh', {}).get('available'):
+        mesh = np.array([[float(v) for v in line.split()[1:4]] for line in (view / c['object_mesh']['data_ref']).read_text().splitlines() if line.startswith('v ')])
+        # Display-only point sampling; full OBJ asset is retained unchanged in data/.
+        mesh = mesh[::max(1, len(mesh)//3000)]
+    for i, frame in enumerate(frames):
+        name = hashlib.sha256(f'{view.relative_to(root)}/{i}'.encode()).hexdigest()[:20]
+        rgb_array = next(videos['rgb'])
+        image = Image.fromarray(rgb_array)
+        overlay = image.copy()
+        if 'segment' in videos:
+            mask = next(videos['segment'])
+            color = np.asarray(COLORS, dtype=np.uint8)[mask]
+            mix = rgb_array.copy()
+            foreground = (mask != 9) & (mask != 0)
+            mix[foreground] = (rgb_array[foreground] * .45 + color[foreground] * .55).astype(np.uint8)
+            overlay = Image.fromarray(mix)
+            Image.fromarray(color).save(target / f'{name}-mask.png')
+        draw = ImageDraw.Draw(overlay)
+        if 'keypoints_uv' in arrays:
+            pts = arrays['keypoints_uv'][i, 1]
+            for a, b in EDGES:
+                if np.isfinite(pts[[a, b]]).all():
+                    draw.line([tuple(pts[a]), tuple(pts[b])], fill=(0, 255, 100), width=2)
+            for k, xy in enumerate(pts):
+                if np.isfinite(xy).all():
+                    x, y = xy
+                    draw.ellipse((x-2, y-2, x+2, y+2), fill=(255, 50, 50))
+                    draw.text((x+3, y), str(k), fill=(255, 255, 255))
+        objcam = None
+        if mesh is not None and 'object_pose' in arrays:
+            pose = arrays['object_pose'][i]
+            objcam = mesh @ pose[:3, :3].T + pose[:3, 3]
+            projected = objcam @ arrays['intrinsic'][i].T
+            for x, y, z in projected[projected[:, 2] > 0]:
+                draw.point((float(x/z), float(y/z)), fill=(255, 210, 0))
+        image.save(target / f'{name}-rgb.png')
+        has_overlay = 'segment' in videos or 'keypoints_uv' in arrays or objcam is not None
+        if has_overlay:
+            overlay.save(target / f'{name}-overlay.png')
+        raw_ref = None
+        if source_root:
+            src = source_root / frame['source_locator']['extracted_path']
+            if sha(src) != frame['source_locator']['extracted_sha256']:
+                raise ValueError('Preview source differs from build source')
+            Image.fromarray(rgb(src)).save(target / f'{name}-source.png')
+            raw_ref = f'{name}-source.png'
+        values = {key: clean_numbers(a[i]) for key, a in arrays.items()}
+        valid = {key: clean_numbers(pt(view / item['valid_ref'])[i]) for key, item in c.items()
+                 if item['available'] and 'valid_ref' in item}
+        cards.append({'id': frame['sample_id'], 'view': view.relative_to(root).as_posix(),
+            'rgb': f'{name}-rgb.png', 'overlay': f'{name}-overlay.png' if has_overlay else None, 'source': raw_ref,
+            'mask': f'{name}-mask.png' if 'segment' in videos else None,
+            'frame': frame, 'arrays': values, 'valid': valid, 'metainfo': m,
+            'annotations': {'segment_instances': instances[i] if instances else None, 'object_metainfo': objects},
+            'object_points_camera': clean_numbers(objcam) if objcam is not None else None,
+            'object_points_canonical': clean_numbers(mesh) if mesh is not None else None})
+    return cards
+
+
+HTML = '''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><title>手物数据预处理检验</title>
+<style>body{font:16px system-ui;margin:24px;background:#101724;color:#ecf3ff}button,select,input{font:inherit;padding:7px}h1{font-size:25px}a{color:#7bd5ff}.grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px}img{width:100%;background:#000}pre{white-space:pre-wrap;overflow-wrap:anywhere;font:13px monospace;max-height:420px;overflow:auto}figure{margin:0}figcaption{padding:6px}canvas{background:#162434;max-width:100%;touch-action:none}.warning{padding:14px;background:#493915}details{padding:10px;background:#1c293c;margin:10px 0}label{display:block;margin:10px 0}</style>
+<h1>手物数据预处理检验</h1><p class="warning">待人工审核。自动无损校验通过 ≠ 标注语义或几何已获人工确认。只展示本报告列出的抽检样本；每个选中视图展示全部帧。未提供模态及原因在元信息中明确列出。</p>
+<p>检查：源图与解码 RGB、左右手、标签颜色、源文件定位、valid、相机参数；有几何时检查 2D 投影和可旋转 3D 点云。绿色为手关键点，黄色为物体。3D 点云仅为显示抽样，完整模型见 data。</p>
+<label>样本 <select id="sample" aria-label="选择检验样本"></select></label><button id="prev">上一帧</button> <button id="next">下一帧</button><span id="counter"></span>
+<h2 id="title"></h2><div class="grid"><figure><img id="source" alt="源 RGB"><figcaption>源文件固定解码 RGB</figcaption></figure><figure><img id="rgb" alt="处理后 RGB"><figcaption>shared MKV 解码 RGB</figcaption></figure><figure><img id="overlay" alt="分割或几何叠加"><figcaption>分割 / 手关键点 / 物体叠加</figcaption></figure><figure><img id="mask" alt="语义标签图"><figcaption>标签图：1 左手红；2 右手蓝；3–8 交互物体；9 源背景灰</figcaption></figure></div>
+<section id="geometry"><p id="geometry-note"></p><div id="camera-geometry"><h2>相机系 3D（拖动旋转）</h2><canvas id="scene" width="700" height="460" aria-label="手物三维点云"></canvas></div><div id="canonical-geometry"><h2>物体规范坐标系 3D（拖动旋转）</h2><canvas id="canonical" width="700" height="460" aria-label="物体规范三维点云"></canvas></div></section>
+<details open><summary>逐帧来源、时间与 split</summary><pre id="frame"></pre></details><details><summary>所有数值标注与有效性</summary><pre id="numbers"></pre></details><details><summary>所有组件 / 缺失原因 / 相机 / 存储与映射</summary><pre id="meta"></pre></details>
+<script>const data=__DATA__;const $=x=>document.getElementById(x);let index=0,yaw=0,pitch=0;
+data.forEach((d,i)=>{let o=document.createElement('option');o.value=i;o.textContent=d.id;$('sample').append(o)});
+function cloud(canvas,points,colors){let ctx=canvas.getContext('2d');ctx.clearRect(0,0,canvas.width,canvas.height);if(!points.length)return;let center=[0,1,2].map(k=>points.reduce((s,p)=>s+p[k],0)/points.length);let q=points.map(p=>p.map((v,k)=>v-center[k]));let scale=180/Math.max(.00001,...q.flat().map(Math.abs));q.forEach((p,i)=>{let x=p[0]*Math.cos(yaw)+p[2]*Math.sin(yaw),z=-p[0]*Math.sin(yaw)+p[2]*Math.cos(yaw),y=p[1]*Math.cos(pitch)-z*Math.sin(pitch);ctx.fillStyle=colors[i];ctx.fillRect(350+x*scale,230+y*scale,3,3)})}
+function redraw(){let d=data[index],hand=(d.arrays.keypoints_xyz||[]).flat().filter(p=>p&&p.every(v=>v!==null)),obj=d.object_points_camera||[];cloud($('scene'),obj.concat(hand),obj.map(()=> '#ffd23f').concat(hand.map(()=> '#50ff9f')));let can=d.object_points_canonical||[];cloud($('canonical'),can,can.map(()=> '#ffd23f'))}
+function show(){let d=data[index];$('sample').value=index;$('title').textContent=d.id;$('counter').textContent=` ${index+1} / ${data.length}`;for(let k of ['source','rgb','overlay','mask']){let el=$(k);el.parentElement.hidden=!d[k];if(d[k])el.src=d[k];}$('frame').textContent=JSON.stringify(d.frame,null,2);$('numbers').textContent=JSON.stringify({arrays:d.arrays,valid:d.valid,annotations:d.annotations},null,2);$('meta').textContent=JSON.stringify(d.metainfo,null,2);$('geometry').hidden=!d.object_points_camera&&!d.object_points_canonical&&!d.arrays.keypoints_xyz;$('camera-geometry').hidden=!d.object_points_camera&&!d.arrays.keypoints_xyz;$('canonical-geometry').hidden=!d.object_points_canonical;$('geometry-note').textContent=d.object_points_canonical&&!d.object_points_camera?'仅有静态物体模型；缺少内参/位姿，不生成相机系叠加。':'';redraw()}
+$('sample').onchange=e=>{index=+e.target.value;show()};$('prev').onclick=()=>{index=(index+data.length-1)%data.length;show()};$('next').onclick=()=>{index=(index+1)%data.length;show()};for(let c of [$('scene'),$('canonical')]){let last=null;c.onpointerdown=e=>{last=[e.clientX,e.clientY];c.setPointerCapture(e.pointerId)};c.onpointerup=()=>last=null;c.onpointermove=e=>{if(last){yaw+=(e.clientX-last[0])*.01;pitch+=(e.clientY-last[1])*.01;last=[e.clientX,e.clientY];redraw()}}}show();</script></html>'''
+
+
+def validate_release(root, per_stratum=4, seed=20260928, all_views=False, source_root=None):
+    build = read_json(root / 'doc/build.json')
+    registry = read_json(root / 'doc/schema.json')
+    results = {}
+    strata = defaultdict(list)
+    for relative in build['views']:
+        m, f = validate_view(root / relative, registry)
+        results[relative] = (m, f)
+        strata[(f[0]['source_split'], m['camera_id'])].append(relative)
+    rng = random.Random(seed)
+    chosen = sorted(results) if all_views or build['phase'] == 'pilot' else sorted(
+        v for s in sorted(strata) for v in rng.sample(strata[s], min(per_stratum, len(strata[s]))))
+    target = root / 'doc/validation'
+    target.mkdir(parents=True, exist_ok=True)
+    # Revalidation invalidates old human review, even if the numeric result stays the same.
+    (target / 'human_review.json').unlink(missing_ok=True)
+    for p in target.glob('*.png'):
+        p.unlink()
+    cards = []
+    for relative in chosen:
+        m, f = results[relative]
+        cards.extend(preview_view(root, root / relative, m, f, target, source_root))
+    payload = json.dumps(cards, ensure_ascii=False, allow_nan=False).replace('<', '\\u003c')
+    (target / 'index.html').write_text(HTML.replace('__DATA__', payload))
+    hashes = {p.relative_to(root).as_posix(): sha(p) for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts
+              and p.relative_to(root).as_posix() not in ('doc/validation/report.json', 'doc/validation/human_review.json')}
+    report = {'dataset': build['dataset'], 'phase': build['phase'], 'recipe': build['recipe'],
+        'automatic_validation_passed': True, 'human_review': 'pending',
+        'all_views_checked': len(results), 'all_frames_checked': sum(m['num_frames'] for m, _ in results.values()),
+        'numeric_scope': 'every component, frame, shape, validity, registered schema and exact video decoded hash',
+        'visual_scope': 'all available modalities; every frame of selected views only',
+        'visual_selected_views': chosen, 'visual_sample_ids': [c['id'] for c in cards],
+        'seed': seed, 'file_hashes': hashes, 'limitations': build['limitations']}
+    (target / 'report.json').write_text(json.dumps(report, ensure_ascii=False, indent=2) + '\n')
+    print(f'Validated {report["all_frames_checked"]} frames; HTML: {target / "index.html"}', flush=True)
+    return report
+
+
+if __name__ == '__main__':
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument('release', type=Path)
+    p.add_argument('--per-stratum', type=int, default=4)
+    p.add_argument('--seed', type=int, default=20260928)
+    p.add_argument('--all', action='store_true', help='Generate previews for every frame of every view; potentially large')
+    p.add_argument('--source-root', type=Path, help='Extracted input, to show source RGB alongside shared RGB')
+    a = p.parse_args()
+    validate_release(a.release, a.per_stratum, a.seed, a.all, a.source_root)