Strip README to preview-minimal; fix lab attribution to robopylab

README.md

@@ -6,153 +6,17 @@ pretty_name: EgoInfinity
 viewer: false
 tags:
   - egocentric
-  - hand-tracking
-  - 3d-scene
-  - video
-  - action-recognition
   - derivative-of-action100m
 ---
 
-# EgoInfinity Dataset
+# EgoInfinity (preview)
 
-Derivative scene assets for a curated subset of [Action100M] (Meta FAIR)
-clips. Used as the data backend for the
-[EgoInfinity Browser](https://huggingface.co/spaces/Rice-RobotPI-Lab/egoinfinity)
-Space.
+Derivative scene assets for a curated subset of Action100M (Meta FAIR) clips.
 
-Source code: <https://github.com/Rice-RobotPI-Lab/EgoInfinity>
-
-[Action100M]: https://github.com/facebookresearch/Action100M
-
-## Contents
-
-```
-samples/
-├── index.json                 # browse-time episode list (consumed by the Space)
-└── <clip_id>/
-    ├── scene.json             # camera intrinsics, object metadata, asset paths
-    ├── signals.json           # per-frame action signals (OR-merged across objects)
-    ├── thumb.jpg              # 320×180 preview rendered from depth
-    ├── recording.viser        # full 3D scene (point cloud + meshes + hands)
-    │
-    │  # Visualization (lossy, fast for streaming)
-    ├── depth.mp4              # MoGe-2 depth, inferno colormap
-    ├── flow.mp4               # MEMFOF optical flow visualization
-    ├── mask.mp4               # SAM-tracked object cutout × original RGB
-    │
-    │  # Hand reconstruction (lossless)
-    ├── hand_joints.bin        # (T, H, 21, 3) float32; 3D joint positions
-    ├── hand_verts.bin         # (T, H, 778, 3) float32; baked MANO vertices
-    ├── hand_faces.bin         # (F, 3) uint16; MANO topology
-    ├── hand_meta.json         # bone connectivity + helper metadata
-    │
-    │  # Object reconstruction (lossless)
-    ├── object_pose.bin        # (T, N_obj, 4, 4) float32; per-frame 6DoF
-    ├── object_obb.bin         # (N_obj, 8, 3) float32; first-valid-frame OBB
-    ├── objects/obj_N.ply      # SAM3D point cloud per object
-    │
-    │  # Raw arrays (lossless, downstream-ready)
-    ├── depth.npz              # (T, H, W) uint16 mm; lossless depth
-    ├── masks.npz              # per-object packed-bit SAM masks
-    ├── bg_template.png        # uint16-mm PNG; bg depth template
-    └── pose_track.json        # full per-object tracker timeseries
-```
-
-## Downloading
-
-This dataset ships per-clip directories of mp4 / npz / bin / ply / json
-files — it is **not** a tabular dataset. The HF auto-loader (`load_dataset(...)`)
-will fail because the per-file JSON schemas are intentionally heterogeneous
-(`scene.json`, `signals.json`, `hand_meta.json`, etc. each describe a
-different aspect of the clip). Use `snapshot_download` instead:
-
-```python
-from huggingface_hub import snapshot_download
-root = snapshot_download(
-    repo_id="Rice-RobotPI-Lab/egoinfinity",
-    repo_type="dataset",
-    # Optional: pull only what you need.
-    # allow_patterns=["samples/index.json", "samples/<clip_id>/*"],
-)
-# root / "samples" / "<clip_id>" now has all assets for that clip.
-```
-
-To grab a single clip:
-
-```python
-from huggingface_hub import hf_hub_download
-hf_hub_download(repo_id="Rice-RobotPI-Lab/egoinfinity",
-                repo_type="dataset",
-                filename="samples/<clip_id>/scene.json")
-```
-
-## Loading raw arrays
-
-```python
-import numpy as np, cv2, json
-
-# Depth (uint16 mm → meters). Sentinel 0 = absent / NaN.
-depth = np.load("depth.npz")["depth"]                # (T, H, W) uint16
-depth_m = depth.astype(np.float32) / 1000.0
-
-# Per-object SAM masks (packed bits per frame per object).
-m = np.load("masks.npz")
-T, H, W = m["_shape"]
-oids = m["_oids"]                                    # ordered object ids
-def mask_for(oid: int, t: int) -> np.ndarray:
-    bits = np.unpackbits(m[f"oid_{oid}"][t])[: H * W]
-    return bits.reshape(H, W).astype(bool)
-
-# Background depth template (rest scene) → meters
-bg = cv2.imread("bg_template.png", cv2.IMREAD_UNCHANGED).astype(np.float32) / 1000.0
-
-# Per-object tracker state: contact_soft, grasp_soft, motion, trust, chamfer,
-# scale_correction, obs_obb_per_frame, etc. Keyed by str(oid).
-pti = json.load(open("pose_track.json"))
-
-# Per-frame 6DoF object pose (camera frame), (T, N_obj, 4, 4) float32
-N_obj = len(json.load(open("scene.json"))["reconstruction"]["objects"])
-poses = np.fromfile("object_pose.bin", dtype=np.float32).reshape(-1, N_obj, 4, 4)
-```
-
-> **Note:** original RGB frames are not redistributed. Anything that needs
-> the source pixels (re-running SAM3 detect, SAM2 track, MEMFOF flow, or
-> SAM3D mesh build) cannot be done from this dataset alone. Algorithms that
-> consume `(depth, masks, hand_*, mesh, pose, bg_template)` (grasp / contact
-> classification, state-machine tuning, ICP-based pose refinement) work
-> standalone.
-
-`<clip_id>` is `<youtube_video_id>_<start_sec>_<end_sec>`. The only original
-YouTube pixels that appear in this repository are inside the SAM-tracked
-object region of `mask.mp4` (everything outside the mask is painted black);
-no full source frames are redistributed.
+**Preview — not for general release.** Schema and contents may change.
 
 ## License
 
-This dataset is released under the FAIR Noncommercial Research License v1
-(see [LICENSE-Action100M](LICENSE-Action100M)) for **noncommercial research
-use only**. Per Section 1.b.ii, redistribution must include a copy of this
-license file.
-
-### Attribution
-
-- **Source clips** are from [Action100M] (Meta FAIR). Full source videos
-  remain on YouTube; only the SAM-tracked region appears in `mask.mp4` as
-  a per-frame cutout.
-- **Depth maps** were generated using MoGe-2.
-- **Optical flow** was computed using MEMFOF.
-- **Object segmentation** uses Meta SAM-3 / SAM-3D.
-- **Hand parameters** were estimated using a WiLoR-based pipeline. Vertex
-  positions are baked from the MANO model (Romero et al., 2017); MANO weights
-  are not redistributed.
-
-## Citation
+FAIR Noncommercial Research License v1 (see [LICENSE-Action100M](LICENSE-Action100M)). Noncommercial research only.
 
-```bibtex
-@misc{egoinfinity2026,
-  title  = {EgoInfinity: A Web-Scale Data Engine for Video-to-Action Robot Learning through Egocentric Views},
-  author = {Rice Robot Perception \& Intelligence Lab},
-  year   = {2026},
-  note   = {Preview release}
-}
-```
+Built by **robopylab**.