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@@ -26432,3 +26432,4 @@ OakInk-v2/shared-v1.0/data/scene_04__A008++seq__f93352845016c27d0fdc__2023-05-09
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diff --git a/OakInk-v2/shared-v1.0/doc/CONTRACT.md b/OakInk-v2/shared-v1.0/doc/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/OakInk-v2/shared-v1.0/doc/RAW_MAPPING.md b/OakInk-v2/shared-v1.0/doc/RAW_MAPPING.md
new file mode 100644
index 0000000000000000000000000000000000000000..83474472915f039dc35fece9c6e04c9072c3d52a
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/doc/RAW_MAPPING.md
@@ -0,0 +1,62 @@
+# OakInk2 raw → shared-v1.0 mapping
+
+This document records the selected OakInk2 fields that are published under
+`OakInk-v2/shared-v1.0`.  The shared release is a normalized training view, not
+a complete copy of every raw field.  Original archives remain under
+`OakInk-v2/raw`.
+
+## Sequence and time axis
+
+- One shared sequence corresponds to one official RGB sequence archive and its
+  annotation pickle.
+- `frame_id_list` is the authoritative RGB row order.  Every camera view stores
+  the source ID and exact camera/MANO/object annotation match in
+  `camera/frames.jsonl`.
+- OakInk2 records four synchronized RGB views at 30 FPS.  `source_fps=30.0` is
+  semantic source timing.  The lossless MKV uses `storage_fps=1` with
+  `timebase_role=index_only`; readers must use `source_fps` and frame mapping for
+  timing.
+- Official train/val/test labels use the pinned OakInk2-TaMF sequence split.
+
+## Selected fields
+
+| Raw source | Raw field/member | Shared output | Conversion |
+| --- | --- | --- | --- |
+| `data/<sequence>.tar` | `<sequence>/<camera>/<frame_id>.png` | `observation/rgb.mkv`, `rgb_valid.pt` | All selected frames, lossless FFV1; decoded RGB is round-trip checked. |
+| `anno_preview/<sequence>.pkl` | `frame_id_list`, `mocap_frame_id_list` | `camera/frames.jsonl` | Exact source IDs and annotation row mapping; no FPS-derived matching. |
+| same pickle | `cam_intr` | `camera/intrinsic.pt`, `intrinsic_valid.pt` | Per-frame pinhole intrinsics, unchanged numeric convention. |
+| same pickle | `cam_extr` | `camera/extrinsic.pt`, `extrinsic_valid.pt` | Per-frame `T_camera_from_world`, metres. |
+| same pickle | `raw_mano` | `annotation/mano/motion.pt`, `motion_valid.pt` | Left/right source quaternion MANO is converted to camera-coordinate axis-angle61 with the official `center_idx=0` wrist convention. |
+| same pickle | `obj_list`, `obj_transf` | `annotation/object/<object_id>/pose.pt`, `pose_valid.pt` | Per-frame `T_camera_from_object`; association remains keyed by the official object ID. |
+| `object_repair.tar` | repaired OBJ/MTL/texture assets | `annotation/object/<object_id>/assets/` and `metainfo.json` | Selected official repair geometry is copied without inferred geometry or scale. |
+| `cam_def`, `cam_selection` | official camera names and selected serials | directory `<camera_id>` plus `metainfo.json:view_type` | `egocentric → ego`; selected allocentric cameras → `exo`. |
+
+All frame-dependent RGB, camera, MANO and object-pose arrays use the same `T`
+and the mapping recorded in `camera/frames.jsonl`.
+
+## Present in raw but not promoted to shared components
+
+| Raw source/field | Status and reason |
+| --- | --- |
+| `object_affordance.tar` | Contains object/part affordance labels, semantic part trees and optional part assets.  It is retained in raw but is not currently an explicit shared training component.  Shared object IDs allow a future versioned join; object identity or geometry must not be treated as an affordance label. |
+| `program.tar`, `program_extension.tar` | Task-language/program annotations are outside this geometric hand-object release. |
+| full-body SMPL-X fields | Outside the selected hand-object reconstruction scope. |
+| contact supervision | Not selected for this release; no contact component is published. |
+| depth | The downloaded official release contains no source depth selected for conversion. |
+| pixel mask/segmentation | The downloaded official release contains no official pixel mask selected for conversion. |
+| unused raw object variants | `object_repair.tar` is the registered geometry source; preview/raw alternatives are retained only in raw. |
+
+Omission means that the field is absent from this shared release, not that it
+does not exist anywhere in the official raw material.
+
+## Provenance and reproducibility
+
+- Converter: `script/preprocess_oakink2.py`
+- Review renderer: `script/render_oakink2_report.py`
+- Full-random renderer: `script/render_oakink2_full_random.py`
+- Runtime versions: `script/requirements-lock.txt`
+- Per-view source locators, component schemas, validity tensors, coordinate
+  conventions and official split evidence are recorded in `metainfo.json`.
+- The content-fingerprint audit fixes the current bytes for future comparison;
+  SHA256 alone does not prove independent official correctness, semantic
+  correctness or visual quality.
diff --git a/OakInk-v2/shared-v1.0/doc/schema.json b/OakInk-v2/shared-v1.0/doc/schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..edb7cf7ce8e8e282d1fbfb5b39ef02a60321d2fb
--- /dev/null
+++ b/OakInk-v2/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"
+  }
+}
diff --git a/OakInk-v2/shared-v1.0/doc/validation/fps-correction/README.md b/OakInk-v2/shared-v1.0/doc/validation/fps-correction/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..45dfde09455d0075df43af5683018dcd1790d1e4
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/doc/validation/fps-correction/README.md
@@ -0,0 +1,7 @@
+# OakInk2 FPS correction
+
+The official CVPR 2024 paper states that the four synchronized RGB views were captured at **30 FPS**. All view metadata now records `source_fps=30.0`. The shared FFV1 MKVs remain unchanged at `storage_fps=1` with `timebase_role=index_only`; this container rate is not capture time.
+
+Review MP4s formerly authored at 20 FPS were retimed to 30 FPS by container stream copy. The encoded H.264 stream SHA256 was checked before and after each retime, so no video frames were re-encoded. The release provides frame IDs but no per-frame timestamps, so `timestamp_ns` remains null.
+
+Evidence: https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_OAKINK2_A_Dataset_of_Bimanual_Hands-Object_Manipulation_in_Complex_Task_CVPR_2024_paper.pdf
diff --git a/OakInk-v2/shared-v1.0/doc/validation/fps-correction/result.json b/OakInk-v2/shared-v1.0/doc/validation/fps-correction/result.json
new file mode 100644
index 0000000000000000000000000000000000000000..e8782abd4dfd81e95f8e9196a00cea545ca6273a
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/doc/validation/fps-correction/result.json
@@ -0,0 +1,195 @@
+{
+  "schema": "oakink2-fps-correction/v1",
+  "completed_at": "2026-09-27T22:18:51.108641+08:00",
+  "source_fps": 30.0,
+  "evidence": "OakInk2 CVPR 2024 paper section 4.1: four synchronized RGB views at 30 fps",
+  "evidence_url": "https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_OAKINK2_A_Dataset_of_Bimanual_Hands-Object_Manipulation_in_Complex_Task_CVPR_2024_paper.pdf",
+  "metainfo_files_changed": 2508,
+  "report_json_files_changed": 5,
+  "html_files_changed": 6,
+  "review_videos": [
+    {
+      "path": "/data1/shaoyunzhe/hand-datasets/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/media/104422070088.mp4",
+      "status": "retimed_stream_copy",
+      "before_fps": 20.0,
+      "after_fps": 29.98,
+      "before_duration": 47.45,
+      "after_duration": 31.65,
+      "encoded_video_sha256": "d6ce8f1c9b7b8ce13caceac1b68810d7d39e976cac089477b94ac8644ef36adb"
+    },
+    {
+      "path": "/data1/shaoyunzhe/hand-datasets/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/media/104422070904.mp4",
+      "status": "retimed_stream_copy",
+      "before_fps": 20.0,
+      "after_fps": 29.98,
+      "before_duration": 47.45,
+      "after_duration": 31.65,
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diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/human-review.json b/OakInk-v2/shared-v1.0/doc/validation/full_random/human-review.json
new file mode 100644
index 0000000000000000000000000000000000000000..cc10b504a754dfbff5cd45e880d23d16243a666d
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/doc/validation/full_random/human-review.json
@@ -0,0 +1,9 @@
+{
+  "dataset_id": "OakInk2",
+  "stage": "full_random",
+  "status": "approved",
+  "reviewed_report": "index.html",
+  "confirmed_at": "2026-09-28T01:50:00+08:00",
+  "confirmed_by": "user",
+  "notes": "User authorized publication after full-random review, FPS correction, official/shared MANO chain comparison, documentation completion, and cache cleanup. Approval covers reviewed samples, not exhaustive manual inspection."
+}
diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/index.html b/OakInk-v2/shared-v1.0/doc/validation/full_random/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..047333a3016046f75e7356b87fac357714eb5854
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/doc/validation/full_random/index.html
@@ -0,0 +1,5 @@
+<!doctype html><html lang='zh-CN'><meta charset='utf-8'><meta name='viewport' content='width=device-width'>
+<title>OakInk2 full random review</title><style>body{font:16px/1.6 system-ui;max-width:1200px;margin:auto;padding:24px;background:#f5f6f8}header,section{background:#fff;padding:20px;margin:16px 0;border-radius:12px}.pending{color:#9a5b00;font-weight:bold}</style>
+<header><h1>OakInk2 全量随机抽查</h1><p class='pending'>人工审核：待确认</p>
+<p>固定随机种子 20260927；按 scene 分层，每层等概率抽取一个完整序列，展示全部相机和全部帧。可视化包含 RGB、相机布局、MANO 与物体网格投影。源数据没有 depth 或 pixel mask。</p>
+<p>SHA256 仅作为当前字节指纹和未来传输比对，不证明源一致性、转换语义或视觉质量。</p><p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header><section><h2>scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39</h2><p>104422070088 (exo, 949 frames), 104422070904 (exo, 949 frames), 104422070969 (ego, 949 frames), 104422071041 (exo, 949 frames)</p><p><a href='scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/index.html'>Open complete-sequence review</a></p></section><section><h2>scene_02__A002++seq__e1fa69abb1738c8fea90__2023-04-17-14-54-52</h2><p>104422070088 (exo, 1144 frames), 104422070904 (exo, 1144 frames), 104422070969 (ego, 1144 frames), 104422071041 (exo, 1144 frames)</p><p><a href='scene_02__A002++seq__e1fa69abb1738c8fea90__2023-04-17-14-54-52/index.html'>Open complete-sequence review</a></p></section><section><h2>scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39</h2><p>104422070088 (exo, 4299 frames), 104422070904 (exo, 4299 frames), 104422070969 (ego, 4299 frames), 104422071041 (exo, 4299 frames)</p><p><a href='scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39/index.html'>Open complete-sequence review</a></p></section><section><h2>scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33</h2><p>104422070088 (exo, 511 frames), 104422070904 (exo, 511 frames), 104422070969 (ego, 511 frames), 104422071041 (exo, 511 frames)</p><p><a href='scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33/index.html'>Open complete-sequence review</a></p></section></html>
\ No newline at end of file
diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/sampling.json b/OakInk-v2/shared-v1.0/doc/validation/full_random/sampling.json
new file mode 100644
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--- /dev/null
+++ b/OakInk-v2/shared-v1.0/doc/validation/full_random/sampling.json
@@ -0,0 +1,21 @@
+{
+  "schema": "hand-warehouse-full-random-sampling/v1",
+  "dataset_id": "OakInk2",
+  "seed": 20260927,
+  "method": "one sequence uniformly sampled per scene stratum",
+  "population_sequences": 627,
+  "strata": {
+    "scene_01": 252,
+    "scene_02": 214,
+    "scene_03": 142,
+    "scene_04": 19
+  },
+  "selected_sequences": [
+    "scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39",
+    "scene_02__A002++seq__e1fa69abb1738c8fea90__2023-04-17-14-54-52",
+    "scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39",
+    "scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33"
+  ],
+  "all_cameras": true,
+  "all_frames": true
+}
\ No newline at end of file
diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/camera-layout.png b/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/camera-layout.png
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@@ -0,0 +1,12 @@
+{
+  "dataset_id": "OakInk2",
+  "release_id": "shared-v1.0",
+  "stage": "full_random",
+  "status": "pending",
+  "review_scope": "complete selected sequence, all cameras, all frames and available geometric modalities",
+  "reviewed_report": "index.html",
+  "reviewed_report_sha256": null,
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+  "confirmed_by": null,
+  "notes": "Automatic checks do not constitute human approval."
+}
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diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/index.html b/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..ca9418e70535b5f816c50661228d5854c7f50435
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+++ b/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/index.html
@@ -0,0 +1,10 @@
+<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width">
+<title>OakInk2 scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39 校验</title><style>body{font:16px/1.6 system-ui;max-width:1500px;margin:auto;padding:24px;background:#f5f6f8}header,section{background:white;padding:20px;margin:16px 0;border-radius:12px}video,img{width:100%;height:auto}table{border-collapse:collapse;overflow:auto;display:block}th,td{padding:6px 10px;border:1px solid #ccd1da;text-align:right}.pending{color:#9a5b00;font-weight:bold}</style>
+<header><h1>OakInk2 · scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39</h1><p class="pending">自动校验完成；人工审核待确认。</p>
+<p>所有选中相机和全部帧均已渲染。源中没有 depth 或像素 mask，因此报告不展示这些模态；物体模型来自官方 object_repair。</p>
+<p>右栏手部使用完整 MANO 三角网格；物体使用 OBJ 全部三角面，而非稀疏顶点。颜色仅用于区分几何实体。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header>
+<section><h2>多相机相对位置</h2><img src="camera-layout.png" alt="camera layout"><p>由首个源帧的 <code>T_camera_from_world</code> 反算相机中心和朝向；坐标为世界坐标，距离单位为米。</p>
+<table><thead><tr><th>baseline (m)</th><th>104422070088</th><th>104422070904</th><th>104422070969</th><th>104422071041</th></tr></thead><tbody><tr><th>104422070088</th><td>0.000</td><td>1.675</td><td>1.244</td><td>1.241</td></tr><tr><th>104422070904</th><td>1.675</td><td>0.000</td><td>1.114</td><td>0.758</td></tr><tr><th>104422070969</th><td>1.244</td><td>1.114</td><td>0.000</td><td>1.201</td></tr><tr><th>104422071041</th><td>1.241</td><td>0.758</td><td>1.201</td><td>0.000</td></tr></tbody></table>
+<h3>序列内相机中心最大位移</h3><table><thead><tr><th>camera</th><th>view</th><th>max drift (m)</th></tr></thead><tbody><tr><th>104422070088</th><td>exo</td><td>0.000</td></tr><tr><th>104422070904</th><td>exo</td><td>0.000</td></tr><tr><th>104422070969</th><td>ego</td><td>0.243</td></tr><tr><th>104422071041</th><td>exo</td><td>0.000</td></tr></tbody></table>
+<p>外部固定相机的数值误差接近零；ego 相机会随头部运动，因此首帧布局不能代表整段中的唯一位置。</p></section><section><h2>104422070088 · exo</h2><video controls preload="metadata" src="media/104422070088.mp4"></video><p>全部 949 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[949, 949]；物体=O02@0010@00003, O02@0017@00002, O02@0032@00001, O02@0032@00002。</p></section><section><h2>104422070904 · exo</h2><video controls preload="metadata" src="media/104422070904.mp4"></video><p>全部 949 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[949, 949]；物体=O02@0010@00003, O02@0017@00002, O02@0032@00001, O02@0032@00002。</p></section><section><h2>104422070969 · ego</h2><video controls preload="metadata" src="media/104422070969.mp4"></video><p>全部 949 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[949, 949]；物体=O02@0010@00003, O02@0017@00002, O02@0032@00001, O02@0032@00002。</p></section><section><h2>104422071041 · exo</h2><video controls preload="metadata" src="media/104422071041.mp4"></video><p>全部 949 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[949, 949]；物体=O02@0010@00003, O02@0017@00002, O02@0032@00001, O02@0032@00002。</p></section></html>
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+}
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diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/sampling.json b/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39/sampling.json
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+  "all_frames": true
+}
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+{
+  "dataset_id": "OakInk2",
+  "release_id": "shared-v1.0",
+  "stage": "full_random",
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+  "reviewed_report": "index.html",
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+  "notes": "Automatic checks do not constitute human approval."
+}
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@@ -0,0 +1,10 @@
+<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width">
+<title>OakInk2 scene_02__A002++seq__e1fa69abb1738c8fea90__2023-04-17-14-54-52 校验</title><style>body{font:16px/1.6 system-ui;max-width:1500px;margin:auto;padding:24px;background:#f5f6f8}header,section{background:white;padding:20px;margin:16px 0;border-radius:12px}video,img{width:100%;height:auto}table{border-collapse:collapse;overflow:auto;display:block}th,td{padding:6px 10px;border:1px solid #ccd1da;text-align:right}.pending{color:#9a5b00;font-weight:bold}</style>
+<header><h1>OakInk2 · scene_02__A002++seq__e1fa69abb1738c8fea90__2023-04-17-14-54-52</h1><p class="pending">自动校验完成；人工审核待确认。</p>
+<p>所有选中相机和全部帧均已渲染。源中没有 depth 或像素 mask，因此报告不展示这些模态；物体模型来自官方 object_repair。</p>
+<p>右栏手部使用完整 MANO 三角网格；物体使用 OBJ 全部三角面，而非稀疏顶点。颜色仅用于区分几何实体。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header>
+<section><h2>多相机相对位置</h2><img src="camera-layout.png" alt="camera layout"><p>由首个源帧的 <code>T_camera_from_world</code> 反算相机中心和朝向；坐标为世界坐标，距离单位为米。</p>
+<table><thead><tr><th>baseline (m)</th><th>104422070088</th><th>104422070904</th><th>104422070969</th><th>104422071041</th></tr></thead><tbody><tr><th>104422070088</th><td>0.000</td><td>1.675</td><td>1.185</td><td>1.242</td></tr><tr><th>104422070904</th><td>1.675</td><td>0.000</td><td>1.113</td><td>0.759</td></tr><tr><th>104422070969</th><td>1.185</td><td>1.113</td><td>0.000</td><td>1.202</td></tr><tr><th>104422071041</th><td>1.242</td><td>0.759</td><td>1.202</td><td>0.000</td></tr></tbody></table>
+<h3>序列内相机中心最大位移</h3><table><thead><tr><th>camera</th><th>view</th><th>max drift (m)</th></tr></thead><tbody><tr><th>104422070088</th><td>exo</td><td>0.000</td></tr><tr><th>104422070904</th><td>exo</td><td>0.000</td></tr><tr><th>104422070969</th><td>ego</td><td>0.128</td></tr><tr><th>104422071041</th><td>exo</td><td>0.000</td></tr></tbody></table>
+<p>外部固定相机的数值误差接近零；ego 相机会随头部运动，因此首帧布局不能代表整段中的唯一位置。</p></section><section><h2>104422070088 · exo</h2><video controls preload="metadata" src="media/104422070088.mp4"></video><p>全部 1144 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[1144, 1144]；物体=O02@0035@00001, O02@0035@00002, O02@0056@00001, O02@0056@00002。</p></section><section><h2>104422070904 · exo</h2><video controls preload="metadata" src="media/104422070904.mp4"></video><p>全部 1144 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[1144, 1144]；物体=O02@0035@00001, O02@0035@00002, O02@0056@00001, O02@0056@00002。</p></section><section><h2>104422070969 · ego</h2><video controls preload="metadata" src="media/104422070969.mp4"></video><p>全部 1144 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[1144, 1144]；物体=O02@0035@00001, O02@0035@00002, O02@0056@00001, O02@0056@00002。</p></section><section><h2>104422071041 · exo</h2><video controls preload="metadata" src="media/104422071041.mp4"></video><p>全部 1144 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[1144, 1144]；物体=O02@0035@00001, O02@0035@00002, O02@0056@00001, O02@0056@00002。</p></section></html>
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+<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width">
+<title>OakInk2 scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39 校验</title><style>body{font:16px/1.6 system-ui;max-width:1500px;margin:auto;padding:24px;background:#f5f6f8}header,section{background:white;padding:20px;margin:16px 0;border-radius:12px}video,img{width:100%;height:auto}table{border-collapse:collapse;overflow:auto;display:block}th,td{padding:6px 10px;border:1px solid #ccd1da;text-align:right}.pending{color:#9a5b00;font-weight:bold}</style>
+<header><h1>OakInk2 · scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39</h1><p class="pending">自动校验完成；人工审核待确认。</p>
+<p>所有选中相机和全部帧均已渲染。源中没有 depth 或像素 mask，因此报告不展示这些模态；物体模型来自官方 object_repair。</p>
+<p>右栏手部使用完整 MANO 三角网格；物体使用 OBJ 全部三角面，而非稀疏顶点。颜色仅用于区分几何实体。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header>
+<section><h2>多相机相对位置</h2><img src="camera-layout.png" alt="camera layout"><p>由首个源帧的 <code>T_camera_from_world</code> 反算相机中心和朝向；坐标为世界坐标，距离单位为米。</p>
+<table><thead><tr><th>baseline (m)</th><th>104422070088</th><th>104422070904</th><th>104422070969</th><th>104422071041</th></tr></thead><tbody><tr><th>104422070088</th><td>0.000</td><td>1.668</td><td>1.209</td><td>1.233</td></tr><tr><th>104422070904</th><td>1.668</td><td>0.000</td><td>1.065</td><td>0.757</td></tr><tr><th>104422070969</th><td>1.209</td><td>1.065</td><td>0.000</td><td>1.090</td></tr><tr><th>104422071041</th><td>1.233</td><td>0.757</td><td>1.090</td><td>0.000</td></tr></tbody></table>
+<h3>序列内相机中心最大位移</h3><table><thead><tr><th>camera</th><th>view</th><th>max drift (m)</th></tr></thead><tbody><tr><th>104422070088</th><td>exo</td><td>0.000</td></tr><tr><th>104422070904</th><td>exo</td><td>0.000</td></tr><tr><th>104422070969</th><td>ego</td><td>0.331</td></tr><tr><th>104422071041</th><td>exo</td><td>0.000</td></tr></tbody></table>
+<p>外部固定相机的数值误差接近零；ego 相机会随头部运动，因此首帧布局不能代表整段中的唯一位置。</p></section><section><h2>104422070088 · exo</h2><video controls preload="metadata" src="media/104422070088.mp4"></video><p>全部 4299 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[4299, 4299]；物体=O02@0011@00002, O02@0011@00003, O02@0015@00022, O02@0015@00023, O02@0015@00025, O02@0015@00026, O02@0033@00002, O02@0204@00001。</p></section><section><h2>104422070904 · exo</h2><video controls preload="metadata" src="media/104422070904.mp4"></video><p>全部 4299 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[4299, 4299]；物体=O02@0011@00002, O02@0011@00003, O02@0015@00022, O02@0015@00023, O02@0015@00025, O02@0015@00026, O02@0033@00002, O02@0204@00001。</p></section><section><h2>104422070969 · ego</h2><video controls preload="metadata" src="media/104422070969.mp4"></video><p>全部 4299 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[4299, 4299]；物体=O02@0011@00002, O02@0011@00003, O02@0015@00022, O02@0015@00023, O02@0015@00025, O02@0015@00026, O02@0033@00002, O02@0204@00001。</p></section><section><h2>104422071041 · exo</h2><video controls preload="metadata" src="media/104422071041.mp4"></video><p>全部 4299 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[4299, 4299]；物体=O02@0011@00002, O02@0011@00003, O02@0015@00022, O02@0015@00023, O02@0015@00025, O02@0015@00026, O02@0033@00002, O02@0204@00001。</p></section></html>
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+<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width">
+<title>OakInk2 scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33 校验</title><style>body{font:16px/1.6 system-ui;max-width:1500px;margin:auto;padding:24px;background:#f5f6f8}header,section{background:white;padding:20px;margin:16px 0;border-radius:12px}video,img{width:100%;height:auto}table{border-collapse:collapse;overflow:auto;display:block}th,td{padding:6px 10px;border:1px solid #ccd1da;text-align:right}.pending{color:#9a5b00;font-weight:bold}</style>
+<header><h1>OakInk2 · scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33</h1><p class="pending">自动校验完成；人工审核待确认。</p>
+<p>所有选中相机和全部帧均已渲染。源中没有 depth 或像素 mask，因此报告不展示这些模态；物体模型来自官方 object_repair。</p>
+<p>右栏手部使用完整 MANO 三角网格；物体使用 OBJ 全部三角面，而非稀疏顶点。颜色仅用于区分几何实体。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header>
+<section><h2>多相机相对位置</h2><img src="camera-layout.png" alt="camera layout"><p>由首个源帧的 <code>T_camera_from_world</code> 反算相机中心和朝向；坐标为世界坐标，距离单位为米。</p>
+<table><thead><tr><th>baseline (m)</th><th>104422070088</th><th>104422070904</th><th>104422070969</th><th>104422071041</th></tr></thead><tbody><tr><th>104422070088</th><td>0.000</td><td>1.680</td><td>1.141</td><td>1.234</td></tr><tr><th>104422070904</th><td>1.680</td><td>0.000</td><td>1.053</td><td>0.772</td></tr><tr><th>104422070969</th><td>1.141</td><td>1.053</td><td>0.000</td><td>1.113</td></tr><tr><th>104422071041</th><td>1.234</td><td>0.772</td><td>1.113</td><td>0.000</td></tr></tbody></table>
+<h3>序列内相机中心最大位移</h3><table><thead><tr><th>camera</th><th>view</th><th>max drift (m)</th></tr></thead><tbody><tr><th>104422070088</th><td>exo</td><td>0.000</td></tr><tr><th>104422070904</th><td>exo</td><td>0.000</td></tr><tr><th>104422070969</th><td>ego</td><td>0.136</td></tr><tr><th>104422071041</th><td>exo</td><td>0.000</td></tr></tbody></table>
+<p>外部固定相机的数值误差接近零；ego 相机会随头部运动，因此首帧布局不能代表整段中的唯一位置。</p></section><section><h2>104422070088 · exo</h2><video controls preload="metadata" src="media/104422070088.mp4"></video><p>全部 511 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[511, 511]；物体=O02@0025@00001, S10020。</p></section><section><h2>104422070904 · exo</h2><video controls preload="metadata" src="media/104422070904.mp4"></video><p>全部 511 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[511, 511]；物体=O02@0025@00001, S10020。</p></section><section><h2>104422070969 · ego</h2><video controls preload="metadata" src="media/104422070969.mp4"></video><p>全部 511 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[511, 511]；物体=O02@0025@00001, S10020。</p></section><section><h2>104422071041 · exo</h2><video controls preload="metadata" src="media/104422071041.mp4"></video><p>全部 511 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[511, 511]；物体=O02@0025@00001, S10020。</p></section></html>
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diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33/sampling.json b/OakInk-v2/shared-v1.0/doc/validation/full_random/scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33/sampling.json
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+{
+  "method": "complete_sequence",
+  "sequence": "scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33",
+  "all_cameras": true,
+  "all_frames": true
+}
\ No newline at end of file
diff --git a/OakInk-v2/shared-v1.0/doc/validation/full_random/status.json b/OakInk-v2/shared-v1.0/doc/validation/full_random/status.json
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+{
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+  "stage": "complete",
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+  "population_sequences": 627,
+  "selected": [
+    "scene_01__O001++seq__1db61b9e2c1e28152c3c__2023-04-13-19-25-39",
+    "scene_02__A002++seq__e1fa69abb1738c8fea90__2023-04-17-14-54-52",
+    "scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39",
+    "scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33"
+  ],
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+    "scene_03__A007++seq__d4caf5e54f1c91438b57__2023-05-15-20-39-39",
+    "scene_04__A005++seq__c80df7bc6d12b566098a__2023-04-20-11-10-33"
+  ]
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+size 19114
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+{
+  "dataset_id": "OakInk2",
+  "release_id": "shared-v1.0",
+  "stage": "pilot",
+  "status": "approved",
+  "review_scope": "complete selected sequence, all cameras, all frames and available geometric modalities",
+  "reviewed_report": "index.html",
+  "reviewed_report_sha256": "dda6f214502ee1077865cd5740d6e0be02d0ce698e979853164884f4b081932d",
+  "confirmed_at": "2026-09-25T16:39:24.6790107+08:00",
+  "confirmed_by": "user",
+  "notes": "User confirmed the mesh-v2 complete-sequence pilot and explicitly authorized full preprocessing. Approval covers the displayed 287-frame, four-camera pilot, not exhaustive full-output review."
+}
diff --git a/OakInk-v2/shared-v1.0/doc/validation/pilot/scene_02__A008++seq__b908c89b32ec6eadd3b0__2023-04-23-09-33-00/index.html b/OakInk-v2/shared-v1.0/doc/validation/pilot/scene_02__A008++seq__b908c89b32ec6eadd3b0__2023-04-23-09-33-00/index.html
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+<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width">
+<title>OakInk2 scene_02__A008++seq__b908c89b32ec6eadd3b0__2023-04-23-09-33-00 校验</title><style>body{font:16px/1.6 system-ui;max-width:1500px;margin:auto;padding:24px;background:#f5f6f8}header,section{background:white;padding:20px;margin:16px 0;border-radius:12px}video,img{width:100%;height:auto}table{border-collapse:collapse;overflow:auto;display:block}th,td{padding:6px 10px;border:1px solid #ccd1da;text-align:right}.pending{color:#9a5b00;font-weight:bold}</style>
+<header><h1>OakInk2 · scene_02__A008++seq__b908c89b32ec6eadd3b0__2023-04-23-09-33-00</h1><p class="pending">自动校验完成；人工审核待确认。</p>
+<p>所有选中相机和全部帧均已渲染。源中没有 depth 或像素 mask，因此报告不展示这些模态；物体模型来自官方 object_repair。</p>
+<p>右栏手部使用完整 MANO 三角网格；物体使用 OBJ 全部三角面，而非稀疏顶点。颜色仅用于区分几何实体。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header>
+<section><h2>多相机相对位置</h2><img src="camera-layout.png" alt="camera layout"><p>由首个源帧的 <code>T_camera_from_world</code> 反算相机中心和朝向；坐标为世界坐标，距离单位为米。</p>
+<table><thead><tr><th>baseline (m)</th><th>104422070088</th><th>104422070904</th><th>104422070969</th><th>104422071041</th></tr></thead><tbody><tr><th>104422070088</th><td>0.000</td><td>1.681</td><td>1.197</td><td>1.233</td></tr><tr><th>104422070904</th><td>1.681</td><td>0.000</td><td>1.090</td><td>0.773</td></tr><tr><th>104422070969</th><td>1.197</td><td>1.090</td><td>0.000</td><td>1.048</td></tr><tr><th>104422071041</th><td>1.233</td><td>0.773</td><td>1.048</td><td>0.000</td></tr></tbody></table><h3>序列内相机中心最大位移</h3><table><thead><tr><th>camera</th><th>view</th><th>max drift (m)</th></tr></thead><tbody><tr><th>104422070088</th><td>exo</td><td>0.000</td></tr><tr><th>104422070904</th><td>exo</td><td>0.000</td></tr><tr><th>104422070969</th><td>ego</td><td>0.225</td></tr><tr><th>104422071041</th><td>exo</td><td>0.000</td></tr></tbody></table><p>外部固定相机的数值误差接近零；ego 相机会随头部运动，因此首帧布局不能代表整段中的唯一位置。</p></section><section><h2>104422070088 · exo</h2><video controls preload="metadata" src="media/104422070088.mp4"></video><p>全部 287 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[287, 287]；物体=O02@0045@00001, O02@0045@00002。</p></section><section><h2>104422070904 · exo</h2><video controls preload="metadata" src="media/104422070904.mp4"></video><p>全部 287 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[287, 287]；物体=O02@0045@00001, O02@0045@00002。</p></section><section><h2>104422070969 · ego</h2><video controls preload="metadata" src="media/104422070969.mp4"></video><p>全部 287 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[287, 287]；物体=O02@0045@00001, O02@0045@00002。</p></section><section><h2>104422071041 · exo</h2><video controls preload="metadata" src="media/104422071041.mp4"></video><p>全部 287 帧。左栏原图；右栏为 MANO 与物体几何投影。MANO valid 左/右=[287, 287]；物体=O02@0045@00001, O02@0045@00002。</p></section></html>
\ No newline at end of file
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+{
+  "method": "complete_sequence",
+  "sequence": "scene_02__A008++seq__b908c89b32ec6eadd3b0__2023-04-23-09-33-00",
+  "all_cameras": true,
+  "all_frames": true
+}
\ No newline at end of file
diff --git a/OakInk-v2/shared-v1.0/script/README.md b/OakInk-v2/shared-v1.0/script/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e7950c22f84d0075f58fa0334db7c84c90bee8df
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/README.md
@@ -0,0 +1,89 @@
+# OakInk2 conversion
+
+The converter and review renderer are deliberately separate:
+
+- `preprocess_oakink2.py` reads raw archives and writes training data.
+- `render_oakink2_report.py` only reads converted data and writes an offline review report.
+
+The downloaded release contains 627 RGB sequence archives, 627 annotation
+pickles and shared object/program archives. It does not contain source depth or
+pixel masks. The converter therefore writes RGB, camera parameters, MANO,
+object poses and object geometry, and records depth/segment as unavailable in
+each `metainfo.json`.
+
+| Modality | Source availability | Shared output |
+| --- | --- | --- |
+| RGB | Yes, per-camera PNG streams | Lossless FFV1 video plus exact frame mapping |
+| Camera intrinsics/extrinsics | Yes | Per-frame tensors |
+| MANO | Yes | Per-frame left/right camera-coordinate tensors |
+| Object pose | Yes | Per-frame `T_camera_from_object` tensors |
+| Object geometry | Yes, in `object_repair.tar` | OBJ, MTL, textures and flags under each object's `assets/` directory |
+| Depth | No source depth in this release | Omitted and recorded as unavailable |
+| Pixel mask/segmentation | No source pixel mask in this release | Omitted and recorded as unavailable |
+
+`object_affordance.tar` contains object-level affordance assets; it is not a
+per-frame pixel mask. The current hand-object reconstruction profile does not
+promote those optional affordance assets into a training component.
+
+## Runtime
+
+Python 3.12 was tested with the versions in `requirements-lock.txt`. MANO v1.2
+`MANO_LEFT.pkl` and `MANO_RIGHT.pkl` are required for the source wrist-centred
+MANO conversion. They are a licensed local dependency and must stay outside the
+release directory.
+
+On iipl_125 the current paths are:
+
+```bash
+ROOT=/data1/shaoyunzhe/hand-datasets/OakInk-v2
+export PYTHONPATH="$ROOT/tmp/runtime-packages"
+MODELS="$ROOT/tmp/models"
+```
+
+## Commands
+
+Read-only inventory:
+
+```bash
+python3 "$ROOT/shared-v1.0/script/preprocess_oakink2.py" inventory --root "$ROOT"
+```
+
+Convert one complete sequence and all officially selected cameras:
+
+```bash
+python3 "$ROOT/shared-v1.0/script/preprocess_oakink2.py" convert \
+  --root "$ROOT" --mano-model-dir "$MODELS" --workers 1 --ffmpeg-threads 2 \
+  --sequence '<sequence stem>'
+```
+
+Render the separate full-frame pilot report:
+
+```bash
+python3 "$ROOT/shared-v1.0/script/render_oakink2_report.py" \
+  --release "$ROOT/shared-v1.0" --mano-model-dir "$MODELS" \
+  --sequence '<sequence stem>' --stage pilot
+```
+
+After pilot approval, full conversion can use a small number of sequence
+workers. Start with two; each sequence contains four camera FFmpeg jobs and a
+large annotation pickle.
+
+```bash
+OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 \
+python3 "$ROOT/shared-v1.0/script/preprocess_oakink2.py" full \
+  --root "$ROOT" --mano-model-dir "$MODELS" --workers 2 --ffmpeg-threads 2
+```
+
+Completed sequences have checkpoints in `tmp/oakink2-convert/checkpoints`.
+They are reused only when the source identities and converter SHA256 match.
+Partial output remains under `tmp/oakink2-convert/partial` and is replaced on
+the next attempt. Raw files are never modified or deleted.
+
+## Alignment and layout
+
+Output is exactly `data/<sequence_id>/<camera_id>/...`. `frame_id_list` defines
+the RGB timeline. It is checked as a subset of `mocap_frame_id_list`, and each
+row in `camera/frames.jsonl` records an exact `annotation_matches` mapping.
+Unknown source timestamps and FPS remain null. Camera serials whose official
+name is `egocentric` use `view_type=ego`; all official allocentric cameras use
+`view_type=exo`.
diff --git a/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/test.txt b/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3131fc99323ec39b0a57deb3d03cd3891ed76372
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/test.txt
@@ -0,0 +1,157 @@
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\ No newline at end of file
diff --git a/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/train.txt b/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bbff714e35c622527464d2965f4353abcd84fcf2
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/train.txt
@@ -0,0 +1,431 @@
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diff --git a/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/val.txt b/OakInk-v2/shared-v1.0/script/dataset_adapters/oakink2_sources/split/val.txt
new file mode 100644
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+scene_02__A001/seq__9ece9dbeab9d71e2c362__2023-04-22-14-25-38
+scene_02__A002/seq__5c41eca99a8f68254e0d__2023-04-17-14-51-05
+scene_02__A004/seq__9cf448429878c58036ef__2023-05-07-18-47-31
+scene_02__A004/seq__5cb937c572504e2267d0__2023-04-26-20-30-59
+scene_02__A004/seq__c64c93242d84629ea481__2023-04-26-20-54-06
+scene_02__A005/seq__5ced9ba4d054b4039ecb__2023-04-25-20-36-43
+scene_02__A005/seq__1e9952b388e901e173b3__2023-04-20-09-34-29
+scene_02__A005/seq__681840c979d1c250086f__2023-04-20-09-44-47
+scene_02__A005/seq__fb9699b5af2e75883b2a__2023-04-25-18-34-18
+scene_02__A007/seq__126ad7e6ea35c8ec7aaf__2023-04-19-09-35-59
+scene_02__A008/seq__d447873bf1bc844be9a1__2023-04-23-09-35-30
+scene_02__A008/seq__6972749dca2bae65783a__2023-04-23-10-37-07
+scene_03__A004/seq__c50cea3a13b82213994f__2023-04-22-18-29-19
+scene_03__A004/seq__155cbd68c1fdb424f73d__2023-04-22-19-07-45
+scene_03__A004/seq__cf9b2eeea82d3a0ccfdc__2023-04-22-20-33-49
+scene_03__A004/seq__a534562c360e83490669__2023-04-26-18-29-08
+scene_03__A004/seq__81a953a8e44603fbe7cb__2023-04-26-18-39-34
+scene_03__A007/seq__c56852334cd7ee5ec868__2023-04-21-09-37-08
+scene_03__A007/seq__ff220c4666f169c19334__2023-04-19-11-43-56
+scene_03__A007/seq__7e7aa43a16d7e7db01c6__2023-04-20-14-28-51
+scene_03__A007/seq__9f2cba19af8ca0b0bf98__2023-04-19-11-23-20
+scene_03__A007/seq__9d98fb4b811e27f5f9ed__2023-04-19-10-48-13
+scene_04__A008/seq__f93352845016c27d0fdc__2023-05-09-19-52-25
+scene_04__A008/seq__ff070467cd91f735acab__2023-04-23-11-28-29
\ No newline at end of file
diff --git a/OakInk-v2/shared-v1.0/script/fix_oakink2_fps_metadata.py b/OakInk-v2/shared-v1.0/script/fix_oakink2_fps_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..df9242f3309b30397c9d29b9e4ce2ceb46002e22
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/fix_oakink2_fps_metadata.py
@@ -0,0 +1,167 @@
+#!/usr/bin/env python3
+"""Correct OakInk2 source FPS metadata and review playback timing.
+
+The shared FFV1 MKVs intentionally remain at storage_fps=1/index_only.  Review
+MP4s created by the former renderer at 20 FPS are retimed to 30 FPS with stream
+copy, so their encoded H.264 packets are not re-encoded.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import re
+import subprocess
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+PAPER_URL = "https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_OAKINK2_A_Dataset_of_Bimanual_Hands-Object_Manipulation_in_Complex_Task_CVPR_2024_paper.pdf"
+EVIDENCE = "OakInk2 CVPR 2024 paper section 4.1: four synchronized RGB views at 30 fps"
+
+
+def write_json(path: Path, value: object) -> None:
+    path.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False), encoding="utf-8")
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(8 << 20), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def probe(ffmpeg: str, path: Path) -> tuple[float, float]:
+    result = subprocess.run([ffmpeg, "-hide_banner", "-i", str(path)], text=True, capture_output=True)
+    text = result.stderr
+    duration_match = re.search(r"Duration: (\d+):(\d+):(\d+(?:\.\d+)?)", text)
+    fps_match = re.search(r"Video:.*?\b(\d+(?:\.\d+)?) fps\b", text)
+    if not duration_match or not fps_match:
+        raise RuntimeError(f"cannot probe {path}: {text[-2000:]}")
+    hours, minutes, seconds = duration_match.groups()
+    return float(fps_match.group(1)), int(hours) * 3600 + int(minutes) * 60 + float(seconds)
+
+
+def elementary_hash(ffmpeg: str, path: Path) -> str:
+    result = subprocess.run(
+        [ffmpeg, "-v", "error", "-i", str(path), "-map", "0:v:0", "-c", "copy", "-f", "hash", "-hash", "sha256", "-"],
+        text=True, capture_output=True, check=True,
+    )
+    value = result.stdout.strip()
+    if not value.startswith("SHA256="):
+        raise RuntimeError(f"unexpected stream hash for {path}: {value}")
+    return value.split("=", 1)[1]
+
+
+def retime(ffmpeg: str, path: Path) -> dict:
+    before_fps, before_duration = probe(ffmpeg, path)
+    if 29.5 <= before_fps <= 30.5:
+        return {"path": path.as_posix(), "status": "already_30fps", "fps": before_fps, "duration": before_duration}
+    if not 19.5 <= before_fps <= 20.5:
+        raise RuntimeError(f"unexpected review FPS {before_fps}: {path}")
+    before_stream_hash = elementary_hash(ffmpeg, path)
+    temporary = path.with_name(path.stem + ".retime.tmp" + path.suffix)
+    subprocess.run(
+        [ffmpeg, "-v", "error", "-y", "-itsscale", "0.6666666666666666", "-i", str(path),
+         "-map", "0:v:0", "-c", "copy", str(temporary)],
+        check=True,
+    )
+    after_fps, after_duration = probe(ffmpeg, temporary)
+    after_stream_hash = elementary_hash(ffmpeg, temporary)
+    if not 29.5 <= after_fps <= 30.5:
+        raise RuntimeError(f"retimed FPS is {after_fps}: {path}")
+    if before_stream_hash != after_stream_hash:
+        raise RuntimeError(f"encoded H.264 packet hash changed: {path}")
+    expected = before_duration * 2.0 / 3.0
+    if abs(after_duration - expected) > 0.12:
+        raise RuntimeError(f"unexpected duration {before_duration} -> {after_duration}: {path}")
+    temporary.replace(path)
+    return {
+        "path": path.as_posix(), "status": "retimed_stream_copy", "before_fps": before_fps,
+        "after_fps": after_fps, "before_duration": before_duration, "after_duration": after_duration,
+        "encoded_video_sha256": after_stream_hash,
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--release", required=True)
+    parser.add_argument("--ffmpeg", required=True)
+    args = parser.parse_args()
+    release = Path(args.release).resolve()
+    changed_meta = 0
+    for path in sorted((release / "data").glob("*/*/metainfo.json")):
+        meta = json.loads(path.read_text(encoding="utf-8"))
+        sampling = meta["sampling"]
+        if sampling.get("source_fps") != 30.0 or sampling.get("type") != "dense":
+            sampling["type"] = "dense"
+            sampling["source_fps"] = 30.0
+            sampling["source_fps_evidence"] = EVIDENCE
+            sampling["mocap_alignment"] = (
+                "image frame IDs are an exact subset of mocap_frame_id_list; image IDs advance by four "
+                "on the shared source frame clock, while all released 30 fps image frames are retained"
+            )
+            write_json(path, meta)
+            changed_meta += 1
+
+    changed_reports = 0
+    for path in sorted((release / "doc/validation").rglob("report.json")):
+        report = json.loads(path.read_text(encoding="utf-8"))
+        changed = False
+        for row in report.get("views", []):
+            if row.get("source_fps") != 30.0:
+                row["source_fps"] = 30.0
+                changed = True
+        if changed:
+            write_json(path, report)
+            changed_reports += 1
+
+    retimed = [retime(args.ffmpeg, path) for path in sorted((release / "doc/validation").rglob("*.mp4"))]
+
+    old = "真实 FPS 未由本次源发布可靠给出，视频只按索引播放；对齐以原始 frame_id 和 frames.jsonl 为准。"
+    new = "官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。"
+    updated_html = 0
+    for path in sorted((release / "doc/validation").rglob("index.html")):
+        content = path.read_text(encoding="utf-8")
+        revised = content.replace(old, new)
+        if "官方论文说明四路 RGB 同步采集为 30 FPS" not in revised:
+            revised = revised.replace("</header>", f"<p>{new}</p></header>", 1)
+        if revised != content:
+            path.write_text(revised, encoding="utf-8")
+            updated_html += 1
+
+    report_root = release / "doc/validation/fps-correction"
+    report_root.mkdir(parents=True, exist_ok=True)
+    payload = {
+        "schema": "oakink2-fps-correction/v1",
+        "completed_at": datetime.now(timezone.utc).astimezone().isoformat(),
+        "source_fps": 30.0,
+        "evidence": EVIDENCE,
+        "evidence_url": PAPER_URL,
+        "metainfo_files_changed": changed_meta,
+        "report_json_files_changed": changed_reports,
+        "html_files_changed": updated_html,
+        "review_videos": retimed,
+        "shared_mkv_policy": "unchanged: storage_fps=1, timebase_role=index_only",
+        "frame_timestamp_policy": "released annotations contain frame IDs but no per-frame timestamps; timestamp_ns remains null",
+        "script_sha256": sha256(Path(__file__).resolve()),
+    }
+    write_json(report_root / "result.json", payload)
+    (report_root / "README.md").write_text(
+        "# OakInk2 FPS correction\n\n"
+        "The official CVPR 2024 paper states that the four synchronized RGB views were captured at **30 FPS**. "
+        "All view metadata now records `source_fps=30.0`. The shared FFV1 MKVs remain unchanged at "
+        "`storage_fps=1` with `timebase_role=index_only`; this container rate is not capture time.\n\n"
+        "Review MP4s formerly authored at 20 FPS were retimed to 30 FPS by container stream copy. The encoded "
+        "H.264 stream SHA256 was checked before and after each retime, so no video frames were re-encoded. "
+        "The release provides frame IDs but no per-frame timestamps, so `timestamp_ns` remains null.\n\n"
+        f"Evidence: {PAPER_URL}\n",
+        encoding="utf-8",
+    )
+    print(json.dumps({"metainfo_changed": changed_meta, "videos": len(retimed), "report": str(report_root)}, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/OakInk-v2/shared-v1.0/script/oakink2-script-README.md b/OakInk-v2/shared-v1.0/script/oakink2-script-README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e7950c22f84d0075f58fa0334db7c84c90bee8df
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/oakink2-script-README.md
@@ -0,0 +1,89 @@
+# OakInk2 conversion
+
+The converter and review renderer are deliberately separate:
+
+- `preprocess_oakink2.py` reads raw archives and writes training data.
+- `render_oakink2_report.py` only reads converted data and writes an offline review report.
+
+The downloaded release contains 627 RGB sequence archives, 627 annotation
+pickles and shared object/program archives. It does not contain source depth or
+pixel masks. The converter therefore writes RGB, camera parameters, MANO,
+object poses and object geometry, and records depth/segment as unavailable in
+each `metainfo.json`.
+
+| Modality | Source availability | Shared output |
+| --- | --- | --- |
+| RGB | Yes, per-camera PNG streams | Lossless FFV1 video plus exact frame mapping |
+| Camera intrinsics/extrinsics | Yes | Per-frame tensors |
+| MANO | Yes | Per-frame left/right camera-coordinate tensors |
+| Object pose | Yes | Per-frame `T_camera_from_object` tensors |
+| Object geometry | Yes, in `object_repair.tar` | OBJ, MTL, textures and flags under each object's `assets/` directory |
+| Depth | No source depth in this release | Omitted and recorded as unavailable |
+| Pixel mask/segmentation | No source pixel mask in this release | Omitted and recorded as unavailable |
+
+`object_affordance.tar` contains object-level affordance assets; it is not a
+per-frame pixel mask. The current hand-object reconstruction profile does not
+promote those optional affordance assets into a training component.
+
+## Runtime
+
+Python 3.12 was tested with the versions in `requirements-lock.txt`. MANO v1.2
+`MANO_LEFT.pkl` and `MANO_RIGHT.pkl` are required for the source wrist-centred
+MANO conversion. They are a licensed local dependency and must stay outside the
+release directory.
+
+On iipl_125 the current paths are:
+
+```bash
+ROOT=/data1/shaoyunzhe/hand-datasets/OakInk-v2
+export PYTHONPATH="$ROOT/tmp/runtime-packages"
+MODELS="$ROOT/tmp/models"
+```
+
+## Commands
+
+Read-only inventory:
+
+```bash
+python3 "$ROOT/shared-v1.0/script/preprocess_oakink2.py" inventory --root "$ROOT"
+```
+
+Convert one complete sequence and all officially selected cameras:
+
+```bash
+python3 "$ROOT/shared-v1.0/script/preprocess_oakink2.py" convert \
+  --root "$ROOT" --mano-model-dir "$MODELS" --workers 1 --ffmpeg-threads 2 \
+  --sequence '<sequence stem>'
+```
+
+Render the separate full-frame pilot report:
+
+```bash
+python3 "$ROOT/shared-v1.0/script/render_oakink2_report.py" \
+  --release "$ROOT/shared-v1.0" --mano-model-dir "$MODELS" \
+  --sequence '<sequence stem>' --stage pilot
+```
+
+After pilot approval, full conversion can use a small number of sequence
+workers. Start with two; each sequence contains four camera FFmpeg jobs and a
+large annotation pickle.
+
+```bash
+OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 \
+python3 "$ROOT/shared-v1.0/script/preprocess_oakink2.py" full \
+  --root "$ROOT" --mano-model-dir "$MODELS" --workers 2 --ffmpeg-threads 2
+```
+
+Completed sequences have checkpoints in `tmp/oakink2-convert/checkpoints`.
+They are reused only when the source identities and converter SHA256 match.
+Partial output remains under `tmp/oakink2-convert/partial` and is replaced on
+the next attempt. Raw files are never modified or deleted.
+
+## Alignment and layout
+
+Output is exactly `data/<sequence_id>/<camera_id>/...`. `frame_id_list` defines
+the RGB timeline. It is checked as a subset of `mocap_frame_id_list`, and each
+row in `camera/frames.jsonl` records an exact `annotation_matches` mapping.
+Unknown source timestamps and FPS remain null. Camera serials whose official
+name is `egocentric` use `view_type=ego`; all official allocentric cameras use
+`view_type=exo`.
diff --git a/OakInk-v2/shared-v1.0/script/preprocess_oakink2.py b/OakInk-v2/shared-v1.0/script/preprocess_oakink2.py
new file mode 100644
index 0000000000000000000000000000000000000000..cee81e5178cd886e4a28bea25f17bbbedcf81893
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/preprocess_oakink2.py
@@ -0,0 +1,676 @@
+"""Convert OakInk2 into hand-warehouse/1.0.
+
+This file only converts and validates data.  HTML/video review lives in
+``render_oakink2_report.py``.
+
+Examples
+--------
+python preprocess_oakink2.py inventory --root /data1/.../OakInk-v2
+python preprocess_oakink2.py convert --root /data1/.../OakInk-v2 \
+    --mano-model-dir /path/to/mano/models --sequence <sequence-stem>
+python preprocess_oakink2.py full --root /data1/.../OakInk-v2 \
+    --mano-model-dir /path/to/mano/models --workers 2
+"""
+
+from __future__ import annotations
+
+import argparse
+import codecs
+import collections
+import hashlib
+import io
+import json
+import os
+import pickle
+import shutil
+import subprocess
+import tarfile
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from datetime import datetime, timezone
+from pathlib import Path, PurePosixPath
+
+import numpy as np
+import torch
+from PIL import Image
+from scipy.spatial.transform import Rotation
+
+
+SCHEMA_VERSION = "hand-warehouse/1.0"
+RELEASE_ID = "shared-v1.0"
+RAW_REVISION = "85c3bfc9ad4d9a6b159e31ac036cf62c87b29076"
+OAKINK2_SPLIT_REVISION = "4cb9c461b92c032715374bd252c7179c70a14bde"
+
+
+def load_official_splits() -> dict[str, str]:
+    split_root = Path(__file__).resolve().parent / "dataset_adapters" / "oakink2_sources" / "split"
+    result: dict[str, str] = {}
+    for split in ("train", "val", "test"):
+        path = split_root / f"{split}.txt"
+        if not path.is_file():
+            raise FileNotFoundError(f"Missing pinned official OakInk2 split list: {path}")
+        for raw in path.read_text(encoding="utf-8").splitlines():
+            source_id = raw.strip()
+            if not source_id:
+                continue
+            sequence_id = source_id.replace("/", "++")
+            if sequence_id in result:
+                raise RuntimeError(f"OakInk2 sequence occurs in more than one split: {sequence_id}")
+            result[sequence_id] = split
+    if len(result) != 627:
+        raise RuntimeError(f"Expected 627 OakInk2 sequences in official split lists, found {len(result)}")
+    return result
+
+
+OAKINK2_OFFICIAL_SPLITS = load_official_splits()
+
+
+def official_split_metadata(sequence: str) -> dict:
+    if sequence not in OAKINK2_OFFICIAL_SPLITS:
+        raise RuntimeError(f"Sequence is absent from pinned official OakInk2 split lists: {sequence}")
+    split = OAKINK2_OFFICIAL_SPLITS[sequence]
+    return {
+        "split": split,
+        "split_scheme": "OakInk2_TaMF_official_sequence_split",
+        "split_source_locator": {
+            "url": (
+                "https://github.com/oakink/OakInk2-TaMF/blob/"
+                f"{OAKINK2_SPLIT_REVISION}/asset/split/{split}.txt"
+            ),
+            "sequence_key": sequence.replace("++", "/"),
+        },
+    }
+
+
+def write_json(path: Path, value) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False), encoding="utf-8")
+
+
+def write_jsonl(path: Path, rows) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8", newline="\n") as stream:
+        for row in rows:
+            stream.write(json.dumps(row, ensure_ascii=False, allow_nan=False) + "\n")
+
+
+def save_tensor(path: Path, value) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    torch.save(torch.as_tensor(np.asarray(value).copy()), path)
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for block in iter(lambda: stream.read(8 << 20), b""):
+            digest.update(block)
+    return digest.hexdigest()
+
+
+def script_sha256() -> str:
+    return sha256(Path(__file__).resolve())
+
+
+# OakInk2 annotations are pickle files containing only NumPy arrays, tensors and
+# containers.  Do not use pickle.load: it can import arbitrary Python objects.
+def _safe_torch_bytes(data: bytes):
+    return torch.load(io.BytesIO(data), map_location="cpu", weights_only=True)
+
+
+class AnnotationUnpickler(pickle.Unpickler):
+    def find_class(self, module, name):
+        allowed = {
+            ("numpy.core.multiarray", "_reconstruct"): np._core.multiarray._reconstruct,
+            ("numpy._core.multiarray", "_reconstruct"): np._core.multiarray._reconstruct,
+            ("numpy", "ndarray"): np.ndarray,
+            ("numpy", "dtype"): np.dtype,
+            ("numpy.core.numeric", "_frombuffer"): np._core.numeric._frombuffer,
+            ("numpy._core.numeric", "_frombuffer"): np._core.numeric._frombuffer,
+            ("_codecs", "encode"): codecs.encode,
+            ("collections", "OrderedDict"): collections.OrderedDict,
+            ("torch._utils", "_rebuild_tensor_v2"): torch._utils._rebuild_tensor_v2,
+            ("torch.storage", "_load_from_bytes"): _safe_torch_bytes,
+        }
+        if (module, name) not in allowed:
+            raise pickle.UnpicklingError(f"forbidden pickle global: {module}.{name}")
+        return allowed[(module, name)]
+
+
+def load_annotation(path: Path):
+    with path.open("rb") as stream:
+        value = AnnotationUnpickler(stream, encoding="latin1").load()
+    required = {
+        "cam_def", "cam_selection", "frame_id_list", "cam_intr", "cam_extr",
+        "mocap_frame_id_list", "obj_list", "obj_transf", "raw_mano",
+    }
+    missing = required - value.keys()
+    if missing:
+        raise ValueError(f"{path.name}: missing fields {sorted(missing)}")
+    return value
+
+
+class StoredState:
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+
+
+class ManoModelUnpickler(AnnotationUnpickler):
+    def find_class(self, module, name):
+        if (module, name) in {
+            ("chumpy.ch", "Ch"),
+            ("chumpy.reordering", "Select"),
+            ("scipy.sparse.csc", "csc_matrix"),
+        }:
+            return StoredState
+        if (module, name) == ("__builtin__", "set"):
+            return set
+        return super().find_class(module, name)
+
+
+def _materialize_chumpy(value):
+    if not isinstance(value, StoredState):
+        return np.asarray(value)
+    # MANO v1.2 shapedirs is a Chumpy Select object.
+    return np.asarray(value.a.x).reshape(-1)[value.idxs].reshape(value.preferred_shape)
+
+
+def load_mano_data(model_dir: Path, side: str) -> dict:
+    path = model_dir / f"MANO_{side.upper()}.pkl"
+    if not path.is_file():
+        raise FileNotFoundError(f"MANO v1.2 model is required: {path}")
+    with path.open("rb") as stream:
+        data = ManoModelUnpickler(stream, encoding="latin1").load()
+    data["shapedirs"] = _materialize_chumpy(data["shapedirs"])
+    data["v_template"] = _materialize_chumpy(data["v_template"])
+    sparse = data["J_regressor"]
+    if isinstance(sparse, StoredState):
+        regressor = np.zeros(sparse._shape, dtype=np.float64)
+        for column in range(sparse._shape[1]):
+            begin, end = sparse.indptr[column : column + 2]
+            regressor[sparse.indices[begin:end], column] = sparse.data[begin:end]
+        data["J_regressor"] = regressor
+    return data
+
+
+def load_mano_roots(model_dir: Path) -> dict[str, dict[str, np.ndarray]]:
+    result = {}
+    for side in ("left", "right"):
+        data = load_mano_data(model_dir, side)
+        shapedirs = np.asarray(data["shapedirs"], dtype=np.float64)
+        template = np.asarray(data["v_template"], dtype=np.float64)
+        sparse = data["J_regressor"]
+        regressor = np.asarray(sparse.toarray() if hasattr(sparse, "toarray") else sparse)
+        if shapedirs.shape != (778, 3, 10) or template.shape != (778, 3) or regressor.shape != (16, 778):
+            raise ValueError(f"unexpected MANO model shapes for {side}")
+        result[side] = {"shapedirs": shapedirs, "template": template, "wrist": regressor[0]}
+    return result
+
+
+def as_numpy(value) -> np.ndarray:
+    if isinstance(value, torch.Tensor):
+        value = value.detach().cpu().numpy()
+    return np.asarray(value)
+
+
+def lookup(mapping: dict, key: int):
+    if key in mapping:
+        return mapping[key]
+    if str(key) in mapping:
+        return mapping[str(key)]
+    raise KeyError(key)
+
+
+def quaternion_pose(rows: list[dict], prefix: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    quaternion = np.stack([as_numpy(row[f"{prefix}__pose_coeffs"]).reshape(16, 4) for row in rows])
+    # Source convention is [w,x,y,z]; scipy expects [x,y,z,w].
+    pose = Rotation.from_quat(np.roll(quaternion.reshape(-1, 4), -1, axis=1)).as_rotvec().reshape(-1, 48)
+    betas = np.stack([as_numpy(row[f"{prefix}__betas"]).reshape(10) for row in rows])
+    translation = np.stack([as_numpy(row[f"{prefix}__tsl"]).reshape(3) for row in rows])
+    return pose, betas, translation
+
+
+def camera_mano(pose, betas, source_translation, extrinsic, model) -> np.ndarray:
+    shaped = model["template"][None] + np.einsum("vci,ti->tvc", model["shapedirs"], betas)
+    wrist = np.einsum("v,tvc->tc", model["wrist"], shaped)
+    # Official ManoLayer uses center_idx=0.  source_translation places that
+    # centered wrist in world coordinates; shared translation adds to the
+    # ordinary flat-mean MANO output, hence the shape-dependent correction.
+    world_translation = source_translation - wrist
+    rotation = extrinsic[:, :3, :3]
+    camera_pose = pose.copy()
+    root_matrix = Rotation.from_rotvec(pose[:, :3]).as_matrix()
+    camera_pose[:, :3] = Rotation.from_matrix(rotation @ root_matrix).as_rotvec()
+    camera_translation = (
+        np.einsum("tij,tj->ti", rotation, wrist + world_translation)
+        + extrinsic[:, :3, 3]
+        - wrist
+    )
+    return np.concatenate([camera_pose, betas, camera_translation], axis=1).astype(np.float32)
+
+
+def component(data_ref, valid_ref, schema_id, axes, valid_axes, source, **extra):
+    return {
+        "available": True,
+        "data_ref": data_ref,
+        "valid_ref": valid_ref,
+        "schema_id": schema_id,
+        "axes": axes,
+        "valid_axes": valid_axes,
+        "source_locator": source,
+        "provenance": {"kind": "format_conversion"},
+        **extra,
+    }
+
+
+def find_ffmpeg(explicit: str | None) -> str:
+    if explicit:
+        return explicit
+    try:
+        import imageio_ffmpeg
+        return imageio_ffmpeg.get_ffmpeg_exe()
+    except Exception as exc:
+        raise RuntimeError("pass --ffmpeg or install imageio-ffmpeg") from exc
+
+
+def encode_rgb(tf: tarfile.TarFile, members: list[str], target: Path, ffmpeg: str, threads: int):
+    first_bytes = tf.extractfile(members[0]).read()
+    with Image.open(io.BytesIO(first_bytes)) as image:
+        first = np.asarray(image.convert("RGB"))
+    height, width = first.shape[:2]
+    target.parent.mkdir(parents=True, exist_ok=True)
+    command = [
+        ffmpeg, "-hide_banner", "-loglevel", "error", "-y",
+        "-f", "rawvideo", "-pixel_format", "rgb24", "-video_size", f"{width}x{height}",
+        "-framerate", "1", "-i", "pipe:0", "-an", "-c:v", "ffv1", "-level", "3",
+        "-g", "1", "-threads", str(threads), str(target),
+    ]
+    process = subprocess.Popen(command, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
+    source_digest = hashlib.sha256()
+    try:
+        for index, member in enumerate(members):
+            data = first_bytes if index == 0 else tf.extractfile(member).read()
+            with Image.open(io.BytesIO(data)) as image:
+                rgb = np.asarray(image.convert("RGB"))
+            if rgb.shape != (height, width, 3):
+                raise ValueError(f"image size changed: {member} {rgb.shape}")
+            block = np.ascontiguousarray(rgb).tobytes()
+            source_digest.update(block)
+            process.stdin.write(block)
+        process.stdin.close()
+        error = process.stderr.read().decode("utf-8", "replace")
+        code = process.wait()
+    except Exception:
+        process.kill()
+        raise
+    if code:
+        raise RuntimeError(error)
+
+    decoded = subprocess.Popen(
+        [ffmpeg, "-hide_banner", "-loglevel", "error", "-threads", str(threads),
+         "-i", str(target), "-pix_fmt", "rgb24", "-f", "rawvideo", "pipe:1"],
+        stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+    )
+    output_digest = hashlib.sha256()
+    output_bytes = 0
+    while True:
+        block = decoded.stdout.read(8 << 20)
+        if not block:
+            break
+        output_digest.update(block)
+        output_bytes += len(block)
+    error = decoded.stderr.read().decode("utf-8", "replace")
+    if decoded.wait():
+        raise RuntimeError(error)
+    expected_bytes = len(members) * height * width * 3
+    if output_bytes != expected_bytes or output_digest.digest() != source_digest.digest():
+        raise ValueError("RGB round-trip mismatch")
+    return width, height, source_digest.hexdigest()
+
+
+def safe_asset_name(name: str, prefix: str) -> str:
+    relative = PurePosixPath(name).relative_to(PurePosixPath(prefix))
+    if not relative.parts or any(part in {"", ".", ".."} for part in relative.parts):
+        raise ValueError(f"unsafe asset member: {name}")
+    return relative.as_posix()
+
+
+def extract_object_assets(asset_tar: tarfile.TarFile, object_id: str, target: Path) -> Path | None:
+    """Extract one object's immutable assets once for the whole sequence."""
+    prefix = f"object_repair/align_ds/{object_id}/"
+    members = [member for member in asset_tar.getmembers() if member.isfile() and member.name.startswith(prefix)]
+    for member in members:
+        relative = safe_asset_name(member.name, prefix)
+        output = target / relative
+        output.parent.mkdir(parents=True, exist_ok=True)
+        with asset_tar.extractfile(member) as source, output.open("wb") as destination:
+            shutil.copyfileobj(source, destination)
+    models = sorted(path for path in target.rglob("*") if path.suffix.lower() in {".obj", ".ply"})
+    return models[0].relative_to(target) if models else None
+
+
+def hardlink_tree(source: Path, target: Path) -> None:
+    """Mirror immutable assets without storing four camera copies of each file."""
+    for path in source.rglob("*"):
+        relative = path.relative_to(source)
+        output = target / relative
+        if path.is_dir():
+            output.mkdir(parents=True, exist_ok=True)
+        elif path.is_file():
+            output.parent.mkdir(parents=True, exist_ok=True)
+            try:
+                os.link(path, output)
+            except OSError:
+                shutil.copy2(path, output)
+
+
+def manifest_map(root: Path) -> dict[str, dict]:
+    path = root / "tmp/hf-download/manifest.json"
+    if not path.is_file():
+        return {}
+    data = json.loads(path.read_text(encoding="utf-8"))
+    return {row["path"].split("OakInk-v2/", 1)[-1]: row for row in data["files"]}
+
+
+def source_identity(root: Path, sequence: str) -> dict:
+    records = manifest_map(root)
+    paths = [f"raw/data/data/{sequence}.tar", f"raw/data/anno_preview/{sequence}.pkl", "raw/data/object_repair.tar"]
+    result = {}
+    for path in paths:
+        row = records.get(path)
+        file_path = root / path
+        if row:
+            result[path] = {key: row[key] for key in ("size", "sha256", "git_oid") if key in row}
+        else:
+            result[path] = {"size": file_path.stat().st_size, "sha256": sha256(file_path)}
+    return result
+
+
+def convert_sequence(root: Path, output: Path, model_dir: Path, sequence: str, ffmpeg: str, threads: int):
+    raw = root / "raw/data"
+    image_tar_path = raw / "data" / f"{sequence}.tar"
+    annotation_path = raw / "anno_preview" / f"{sequence}.pkl"
+    object_tar_path = raw / "object_repair.tar"
+    for path in (image_tar_path, annotation_path, object_tar_path):
+        if not path.is_file():
+            raise FileNotFoundError(path)
+
+    identity = source_identity(root, sequence)
+    checkpoint_dir = root / "tmp/oakink2-convert/checkpoints"
+    checkpoint = checkpoint_dir / f"{sequence}.json"
+    signature = {"script_sha256": script_sha256(), "source": identity, "output_root": str(output)}
+    if checkpoint.is_file() and (output / "data" / sequence).is_dir():
+        previous = json.loads(checkpoint.read_text(encoding="utf-8"))
+        if previous.get("signature") == signature and previous.get("status") == "complete":
+            return {"sequence": sequence, "status": "skipped", "views": previous["views"]}
+
+    annotation = load_annotation(annotation_path)
+    frame_ids = [int(value) for value in annotation["frame_id_list"]]
+    mocap_ids = {int(value) for value in annotation["mocap_frame_id_list"]}
+    if not frame_ids or not set(frame_ids).issubset(mocap_ids):
+        raise ValueError(f"{sequence}: image frames are not a subset of mocap frames")
+    mano_rows = [lookup(annotation["raw_mano"], frame_id) for frame_id in frame_ids]
+    mano_models = load_mano_roots(model_dir)
+
+    partial = root / "tmp/oakink2-convert/partial" / sequence
+    if partial.exists():
+        shutil.rmtree(partial)
+    partial.mkdir(parents=True)
+    views = []
+    with tarfile.open(image_tar_path, "r:*") as image_tar, tarfile.open(object_tar_path, "r:*") as object_tar:
+        image_members = {member.name: member for member in image_tar.getmembers() if member.isfile()}
+        asset_cache = partial / ".object-assets-cache"
+        object_models = {}
+        for object_id in annotation["obj_list"]:
+            cache_root = asset_cache / object_id
+            object_models[object_id] = extract_object_assets(object_tar, object_id, cache_root)
+        selected_names = set(annotation["cam_selection"])
+        cameras = [serial for serial, name in annotation["cam_def"].items() if name in selected_names]
+        if not cameras:
+            raise ValueError(f"{sequence}: no selected cameras")
+        for serial in cameras:
+            camera_name = annotation["cam_def"][serial]
+            members = [f"{sequence}/{serial}/{frame_id:06d}.png" for frame_id in frame_ids]
+            missing = [member for member in members if member not in image_members]
+            if missing:
+                raise FileNotFoundError(f"{sequence}/{serial}: missing {len(missing)} RGB frames")
+            intrinsic = np.stack([lookup(annotation["cam_intr"][camera_name], frame_id) for frame_id in frame_ids]).astype(np.float32)
+            extrinsic = np.stack([lookup(annotation["cam_extr"][camera_name], frame_id) for frame_id in frame_ids]).astype(np.float32)
+            if intrinsic.shape != (len(frame_ids), 3, 3) or extrinsic.shape != (len(frame_ids), 4, 4):
+                raise ValueError(f"{sequence}/{serial}: invalid camera tensor shape")
+
+            view = partial / serial
+            width, height, rgb_digest = encode_rgb(image_tar, members, view / "observation/rgb.mkv", ffmpeg, threads)
+            valid = np.ones(len(frame_ids), dtype=bool)
+            save_tensor(view / "observation/rgb_valid.pt", valid)
+            save_tensor(view / "camera/intrinsic.pt", intrinsic)
+            save_tensor(view / "camera/intrinsic_valid.pt", valid)
+            save_tensor(view / "camera/extrinsic.pt", extrinsic)
+            save_tensor(view / "camera/extrinsic_valid.pt", valid)
+
+            motion = np.full((len(frame_ids), 2, 61), np.nan, dtype=np.float32)
+            for side_index, (side, prefix) in enumerate((("left", "lh"), ("right", "rh"))):
+                pose, betas, translation = quaternion_pose(mano_rows, prefix)
+                motion[:, side_index] = camera_mano(pose, betas, translation, extrinsic, mano_models[side])
+            mano_valid = np.isfinite(motion).all(axis=2)
+            save_tensor(view / "annotation/mano/motion.pt", motion)
+            save_tensor(view / "annotation/mano/motion_valid.pt", mano_valid)
+
+            object_components = {}
+            for object_id in annotation["obj_list"]:
+                world = np.stack([lookup(annotation["obj_transf"][object_id], frame_id) for frame_id in frame_ids]).astype(np.float32)
+                pose = extrinsic @ world
+                pose_valid = np.isfinite(pose).all(axis=(1, 2))
+                object_root = view / "annotation/object" / object_id
+                save_tensor(object_root / "pose.pt", pose)
+                save_tensor(object_root / "pose_valid.pt", pose_valid)
+                hardlink_tree(asset_cache / object_id, object_root / "assets")
+                model_relative = object_models[object_id]
+                mesh_ref = (object_root / "assets" / model_relative).relative_to(view).as_posix() if model_relative else None
+                write_json(object_root / "metainfo.json", {
+                    "object_id": object_id,
+                    "source_object_id": object_id,
+                    "identity_scope": "sequence",
+                    "scope_id": sequence,
+                    "association_basis": "official OakInk2 obj_list and obj_transf",
+                    "has_model": mesh_ref is not None,
+                    "mesh_ref": mesh_ref,
+                    "unit": "m",
+                    "source_locator": {"archive": "raw/data/object_repair.tar", "object_id": object_id},
+                })
+                object_components[f"object_{object_id}_pose"] = component(
+                    f"annotation/object/{object_id}/pose.pt",
+                    f"annotation/object/{object_id}/pose_valid.pt",
+                    "object/rigid/v1", ["T", "row", "column"], ["T"],
+                    {"file": f"raw/data/anno_preview/{sequence}.pkl", "field": f"obj_transf.{object_id}"},
+                    unit="m", convention="T_camera_from_object", object_type="rigid", entity_id=object_id,
+                )
+
+            frame_rows = []
+            for index, frame_id in enumerate(frame_ids):
+                match = {
+                    "source_frame_id": str(frame_id),
+                    "source_timestamp_ns": None,
+                    "clock_domain": None,
+                    "match_method": "exact",
+                    "delta_ns": None,
+                    "source_record_present": True,
+                }
+                frame_rows.append({
+                    "frame_index": index,
+                    "source_frame_id": str(frame_id),
+                    "timestamp_ns": None,
+                    "clock_domain": None,
+                    "sync_id": f"{sequence}:{frame_id}",
+                    "source_locator": {"archive": f"raw/data/data/{sequence}.tar", "member": members[index]},
+                    "annotation_matches": {"camera": match, "mano": match, "object": match},
+                })
+            write_jsonl(view / "camera/frames.jsonl", frame_rows)
+
+            components = {
+                "rgb": component(
+                    "observation/rgb.mkv", "observation/rgb_valid.pt", "rgb/uint8/v1",
+                    ["T", "height", "width", "channel"], ["T"],
+                    {"archive": f"raw/data/data/{sequence}.tar", "member_pattern": f"{sequence}/{serial}/{{source_frame_id}}.png"},
+                    storage={"profile": "mkv/ffv1_rgb/v1", "codec": "ffv1", "pixel_format": "bgr0",
+                             "decoded_pixel_format": "rgb24", "source_lossy": False,
+                             "roundtrip_rgb_sha256": rgb_digest},
+                    storage_fps=1, timebase_role="index_only",
+                ),
+                "intrinsic": component(
+                    "camera/intrinsic.pt", "camera/intrinsic_valid.pt", "camera/pinhole/v1",
+                    ["T", "row", "column"], ["T"],
+                    {"file": f"raw/data/anno_preview/{sequence}.pkl", "field": f"cam_intr.{camera_name}"},
+                ),
+                "extrinsic": component(
+                    "camera/extrinsic.pt", "camera/extrinsic_valid.pt", "transform/se3/v1",
+                    ["T", "row", "column"], ["T"],
+                    {"file": f"raw/data/anno_preview/{sequence}.pkl", "field": f"cam_extr.{camera_name}"},
+                    unit="m", convention="T_camera_from_world",
+                ),
+                "mano": component(
+                    "annotation/mano/motion.pt", "annotation/mano/motion_valid.pt", "mano/axis_angle61/v1",
+                    ["T", "hand", "parameter"], ["T", "hand"],
+                    {"file": f"raw/data/anno_preview/{sequence}.pkl", "field": "raw_mano",
+                     "time_index_ref": "camera/frames.jsonl:annotation_matches.mano"},
+                    unit="m", angle_unit="rad", coordinate_frame="camera",
+                    model_profile={"version": "MANO-v1.2", "flat_hand_mean": True, "left_shapedirs_fix": False},
+                    provenance={"kind": "format_conversion", "method": "source wxyz quaternion to axis-angle; official center_idx=0 wrist correction; world-to-camera transform"},
+                ),
+                **object_components,
+            }
+            metadata = {
+                "schema_version": SCHEMA_VERSION,
+                "dataset_id": "OakInk2",
+                "release_id": RELEASE_ID,
+                "sequence_id": sequence,
+                "camera_id": serial,
+                "sample_type": "video",
+                "num_frames": len(frame_ids),
+                "hand_slots": ["left", "right"],
+                "sampling": {
+                    "type": "dense",
+                    "source_fps": 30.0,
+                    "frames_ref": "camera/frames.jsonl",
+                    "selection": "all official frame_id_list images for this complete sequence and selected camera",
+                    "source_fps_evidence": "OakInk2 CVPR 2024 paper section 4.1: four synchronized RGB views at 30 fps",
+                    "mocap_alignment": "image frame IDs are an exact subset of mocap_frame_id_list; image IDs advance by four on the shared source frame clock, while all released 30 fps image frames are retained",
+                },
+                "camera": {"model": "pinhole", "schema_id": "camera/pinhole/v1", "image_size": [width, height]},
+                "components": components,
+                **official_split_metadata(sequence),
+                "view_type": "ego" if camera_name == "egocentric" else "exo",
+                "source_camera_name": camera_name,
+                "omitted_components": {
+                    "depth": "not provided by the downloaded official OakInk2 release",
+                    "segment": "no official pixel mask is present in the downloaded release",
+                    "contact": "not selected for this hand-object reconstruction release",
+                    "smplx": "full-body parameters are outside the selected hand-object reconstruction fields",
+                    "program": "task-language/program annotations are outside the selected geometric training fields",
+                },
+                "source_release": {"repository": "ParityLab/Hand-Datasets-Raw", "revision": RAW_REVISION},
+            }
+            write_json(view / "metainfo.json", metadata)
+            views.append({"camera_id": serial, "view_type": metadata["view_type"], "frames": len(frame_ids)})
+
+        shutil.rmtree(asset_cache)
+
+    final = output / "data" / sequence
+    final.parent.mkdir(parents=True, exist_ok=True)
+    if final.exists():
+        shutil.rmtree(final)
+    partial.replace(final)
+    write_json(checkpoint, {"status": "complete", "sequence": sequence, "views": views, "signature": signature})
+    return {"sequence": sequence, "status": "converted", "views": views}
+
+
+def sequence_names(root: Path) -> list[str]:
+    annotation_root = root / "raw/data/anno_preview"
+    image_root = root / "raw/data/data"
+    annotations = {path.stem for path in annotation_root.glob("*.pkl")}
+    images = {path.stem for path in image_root.glob("*.tar")}
+    if annotations != images:
+        raise ValueError(f"annotation/image mismatch: annotation-only={len(annotations-images)}, image-only={len(images-annotations)}")
+    return sorted(annotations)
+
+
+def run_convert(args, names: list[str]) -> None:
+    root = Path(args.root).resolve()
+    output = Path(args.output or root / RELEASE_ID).resolve()
+    model_dir = Path(args.mano_model_dir).resolve()
+    ffmpeg = find_ffmpeg(args.ffmpeg)
+    runtime = root / "tmp/oakink2-convert"
+    status_path = runtime / "status.json"
+    lock_path = runtime / "run.lock"
+    runtime.mkdir(parents=True, exist_ok=True)
+    lock_stream = lock_path.open("a+", encoding="utf-8")
+    try:
+        import fcntl
+        fcntl.flock(lock_stream.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+    except (ImportError, BlockingIOError) as error:
+        lock_stream.close()
+        raise RuntimeError(f"another OakInk2 converter holds {lock_path}") from error
+    started_at = datetime.now(timezone.utc).isoformat()
+    state = {
+        "stage": "running", "started_at": started_at, "updated_at": started_at,
+        "pid": os.getpid(), "total_sequences": len(names), "completed_sequences": 0,
+        "workers": args.workers, "ffmpeg_threads": args.ffmpeg_threads,
+        "output": str(output), "last_result": None,
+    }
+    write_json(status_path, state)
+    try:
+        if args.workers == 1:
+            iterator = (convert_sequence(root, output, model_dir, name, ffmpeg, args.ffmpeg_threads) for name in names)
+        else:
+            pool = ProcessPoolExecutor(max_workers=args.workers)
+            futures = [pool.submit(convert_sequence, root, output, model_dir, name, ffmpeg, args.ffmpeg_threads) for name in names]
+            iterator = (future.result() for future in as_completed(futures))
+        for result in iterator:
+            print(json.dumps(result), flush=True)
+            state["completed_sequences"] += 1
+            state["last_result"] = result
+            state["updated_at"] = datetime.now(timezone.utc).isoformat()
+            write_json(status_path, state)
+        if args.workers != 1:
+            pool.shutdown()
+        state["stage"] = "complete"
+    except Exception as error:
+        state["stage"] = "failed"
+        state["error_type"] = type(error).__name__
+        state["error"] = str(error)
+        raise
+    finally:
+        state["updated_at"] = datetime.now(timezone.utc).isoformat()
+        write_json(status_path, state)
+        lock_stream.close()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    sub = parser.add_subparsers(dest="command", required=True)
+    inventory = sub.add_parser("inventory")
+    inventory.add_argument("--root", required=True)
+    for command in ("convert", "full"):
+        item = sub.add_parser(command)
+        item.add_argument("--root", required=True)
+        item.add_argument("--output")
+        item.add_argument("--mano-model-dir", required=True)
+        item.add_argument("--ffmpeg")
+        item.add_argument("--ffmpeg-threads", type=int, default=2)
+        item.add_argument("--workers", type=int, default=1)
+        if command == "convert":
+            item.add_argument("--sequence", action="append", required=True)
+    args = parser.parse_args()
+    root = Path(args.root).resolve()
+    names = sequence_names(root)
+    if args.command == "inventory":
+        print(json.dumps({"sequences": len(names), "first": names[:10]}, indent=2))
+    elif args.command == "convert":
+        unknown = sorted(set(args.sequence) - set(names))
+        if unknown:
+            raise ValueError(f"unknown sequences: {unknown}")
+        run_convert(args, args.sequence)
+    else:
+        run_convert(args, names)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/OakInk-v2/shared-v1.0/script/render_oakink2_full_random.py b/OakInk-v2/shared-v1.0/script/render_oakink2_full_random.py
new file mode 100644
index 0000000000000000000000000000000000000000..27625592920b290230c0cd720c895fb275742a37
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/render_oakink2_full_random.py
@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""Render a deterministic stratified OakInk2 full-output review."""
+
+from __future__ import annotations
+
+import argparse
+import html
+import json
+import random
+from pathlib import Path
+from types import SimpleNamespace
+
+import render_oakink2_report as renderer
+
+
+def write_json(path: Path, value: object) -> None:
+    renderer.write_json(path, value)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--release", required=True)
+    parser.add_argument("--mano-model-dir", required=True)
+    parser.add_argument("--output", required=True)
+    parser.add_argument("--seed", type=int, default=20260927)
+    parser.add_argument("--ffmpeg")
+    args = parser.parse_args()
+    release = Path(args.release).resolve()
+    output = Path(args.output).resolve()
+    output.mkdir(parents=True, exist_ok=True)
+    groups: dict[str, list[str]] = {}
+    for path in sorted((release / "data").iterdir()):
+        if path.is_dir():
+            scene = path.name.split("__", 1)[0]
+            groups.setdefault(scene, []).append(path.name)
+    rng = random.Random(args.seed)
+    selected = [rng.choice(groups[scene]) for scene in sorted(groups)]
+    status_path = output / "status.json"
+    status = {
+        "schema": "oakink2-full-random-status/v1", "stage": "rendering",
+        "seed": args.seed, "sampling": "one complete sequence uniformly sampled within each scene stratum",
+        "population_sequences": sum(map(len, groups.values())), "selected": selected, "completed": [],
+    }
+    write_json(status_path, status)
+    cards = []
+    for sequence in selected:
+        target = output / sequence
+        if not (target / "report.json").is_file():
+            renderer.render(SimpleNamespace(
+                release=str(release), sequence=sequence, mano_model_dir=args.mano_model_dir,
+                ffmpeg=args.ffmpeg, output=str(target), stage="full_random",
+            ))
+        report = json.loads((target / "report.json").read_text(encoding="utf-8"))
+        status["completed"].append(sequence)
+        write_json(status_path, status)
+        rows = ", ".join(f"{row['camera_id']} ({row['view_type']}, {row['frames']} frames)" for row in report["views"])
+        cards.append(
+            f"<section><h2>{html.escape(sequence)}</h2><p>{html.escape(rows)}</p>"
+            f"<p><a href='{html.escape(sequence)}/index.html'>Open complete-sequence review</a></p></section>"
+        )
+    sampling = {
+        "schema": "hand-warehouse-full-random-sampling/v1", "dataset_id": "OakInk2",
+        "seed": args.seed, "method": "one sequence uniformly sampled per scene stratum",
+        "population_sequences": sum(map(len, groups.values())),
+        "strata": {key: len(value) for key, value in sorted(groups.items())},
+        "selected_sequences": selected, "all_cameras": True, "all_frames": True,
+    }
+    write_json(output / "sampling.json", sampling)
+    write_json(output / "human-review.json", {
+        "dataset_id": "OakInk2", "stage": "full_random", "status": "pending",
+        "reviewed_report": "index.html", "confirmed_at": None, "confirmed_by": None,
+        "notes": "Automatic conversion checks and content fingerprints do not constitute human approval.",
+    })
+    page = f"""<!doctype html><html lang='zh-CN'><meta charset='utf-8'><meta name='viewport' content='width=device-width'>
+<title>OakInk2 full random review</title><style>body{{font:16px/1.6 system-ui;max-width:1200px;margin:auto;padding:24px;background:#f5f6f8}}header,section{{background:#fff;padding:20px;margin:16px 0;border-radius:12px}}.pending{{color:#9a5b00;font-weight:bold}}</style>
+<header><h1>OakInk2 全量随机抽查</h1><p class='pending'>人工审核：待确认</p>
+<p>固定随机种子 {args.seed}；按 scene 分层，每层等概率抽取一个完整序列，展示全部相机和全部帧。可视化包含 RGB、相机布局、MANO 与物体网格投影。源数据没有 depth 或 pixel mask。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；所有审查视频按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基。</p>
+<p>SHA256 仅作为当前字节指纹和未来传输比对，不证明源一致性、转换语义或视觉质量。</p></header>{''.join(cards)}</html>"""
+    (output / "index.html").write_text(page, encoding="utf-8")
+    status["stage"] = "complete"
+    write_json(status_path, status)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/OakInk-v2/shared-v1.0/script/render_oakink2_report.py b/OakInk-v2/shared-v1.0/script/render_oakink2_report.py
new file mode 100644
index 0000000000000000000000000000000000000000..15df85693f41390ada2390f94ecb7a41d288456b
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/render_oakink2_report.py
@@ -0,0 +1,354 @@
+"""Render an offline OakInk2 review report from converted shared data.
+
+This script never converts or edits training tensors.  It reads a complete
+converted sequence, renders every frame for every camera, and writes only under
+``doc/validation``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import subprocess
+import sys
+from types import SimpleNamespace
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image, ImageDraw
+import smplx
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from preprocess_oakink2 import load_mano_data
+
+
+HAND_COLORS = ((30, 210, 235), (255, 120, 55))
+OBJECT_COLORS = ((85, 235, 75), (190, 95, 245), (255, 215, 70), (80, 150, 255))
+CAMERA_COLORS = ((40, 110, 230), (230, 80, 65), (60, 175, 95), (180, 95, 210), (230, 160, 45))
+
+
+def load_json(path: Path):
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def write_json(path: Path, value) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False), encoding="utf-8")
+
+
+def load_tensor(path: Path) -> np.ndarray:
+    return torch.load(path, map_location="cpu", weights_only=True).numpy()
+
+
+def find_ffmpeg(explicit: str | None) -> str:
+    if explicit:
+        return explicit
+    import imageio_ffmpeg
+    return imageio_ffmpeg.get_ffmpeg_exe()
+
+
+def decode_video(ffmpeg: str, path: Path, width: int, height: int):
+    process = subprocess.Popen(
+        [ffmpeg, "-hide_banner", "-loglevel", "error", "-i", str(path),
+         "-pix_fmt", "rgb24", "-f", "rawvideo", "pipe:1"],
+        stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+    )
+    frame_bytes = width * height * 3
+    while True:
+        data = process.stdout.read(frame_bytes)
+        if not data:
+            break
+        if len(data) != frame_bytes:
+            raise ValueError(f"truncated decoded frame: {path}")
+        yield np.frombuffer(data, dtype=np.uint8).reshape(height, width, 3).copy()
+    error = process.stderr.read().decode("utf-8", "replace")
+    if process.wait():
+        raise RuntimeError(error)
+
+
+def video_writer(ffmpeg: str, path: Path, width: int, height: int, fps: int = 30):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    command = [
+        ffmpeg, "-hide_banner", "-loglevel", "error", "-y", "-f", "rawvideo",
+        "-pixel_format", "rgb24", "-video_size", f"{width}x{height}", "-framerate", str(fps),
+        "-i", "pipe:0", "-an", "-c:v", "libx264", "-crf", "20", "-pix_fmt", "yuv420p", str(path),
+    ]
+    return subprocess.Popen(command, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
+
+
+def mano_meshes(model_dir: Path, motion: np.ndarray, valid: np.ndarray):
+    meshes = []
+    for side_index, side in enumerate(("left", "right")):
+        model = smplx.MANO(
+            str(model_dir), data_struct=SimpleNamespace(**load_mano_data(model_dir, side)),
+            is_rhand=side == "right", use_pca=False, flat_hand_mean=True,
+        )
+        result = np.full((len(motion), 778, 3), np.nan, dtype=np.float32)
+        indices = np.flatnonzero(valid[:, side_index])
+        for begin in range(0, len(indices), 64):
+            selected = indices[begin : begin + 64]
+            values = torch.from_numpy(motion[selected, side_index]).float()
+            with torch.no_grad():
+                output = model(
+                    global_orient=values[:, :3], hand_pose=values[:, 3:48],
+                    betas=values[:, 48:58], transl=values[:, 58:61],
+                )
+            result[selected] = output.vertices.cpu().numpy()
+        meshes.append((result, np.asarray(model.faces, dtype=np.int32)))
+    return meshes
+
+
+def obj_mesh(path: Path) -> tuple[np.ndarray, np.ndarray]:
+    points = []
+    triangles = []
+    with path.open("r", encoding="utf-8", errors="replace") as stream:
+        for line in stream:
+            if line.startswith("v "):
+                values = line.split()
+                points.append([float(values[1]), float(values[2]), float(values[3])])
+            elif line.startswith("f "):
+                indices = []
+                for value in line.split()[1:]:
+                    index = int(value.split("/", 1)[0])
+                    indices.append(index - 1 if index > 0 else len(points) + index)
+                for offset in range(1, len(indices) - 1):
+                    triangles.append((indices[0], indices[offset], indices[offset + 1]))
+    vertices = np.asarray(points, dtype=np.float32)
+    faces = np.asarray(triangles, dtype=np.int32)
+    if not len(vertices) or not len(faces) or faces.min() < 0 or faces.max() >= len(vertices):
+        raise ValueError(f"invalid or empty OBJ mesh: {path}")
+    return vertices, faces
+
+
+def project(points: np.ndarray, intrinsic: np.ndarray) -> np.ndarray:
+    pixel = points @ intrinsic.T
+    return pixel[:, :2] / pixel[:, 2:3]
+
+
+def overlay_mesh(image: Image.Image, points: np.ndarray, faces: np.ndarray, intrinsic: np.ndarray, color) -> None:
+    uv = project(points, intrinsic)
+    triangles = faces[
+        np.isfinite(points[faces]).all(axis=(1, 2))
+        & (points[faces, 2] > 1e-4).all(axis=1)
+        & np.isfinite(uv[faces]).all(axis=(1, 2))
+    ]
+    if not len(triangles):
+        return
+    triangle_uv = uv[triangles]
+    visible = (
+        (triangle_uv[:, :, 0].max(axis=1) >= -image.width)
+        & (triangle_uv[:, :, 0].min(axis=1) <= image.width * 2)
+        & (triangle_uv[:, :, 1].max(axis=1) >= -image.height)
+        & (triangle_uv[:, :, 1].min(axis=1) <= image.height * 2)
+    )
+    triangles = triangles[visible]
+    if not len(triangles):
+        return
+    order = np.argsort(points[triangles, 2].mean(axis=1))[::-1]
+    layer = Image.new("RGBA", image.size, (0, 0, 0, 0))
+    draw = ImageDraw.Draw(layer, "RGBA")
+    fill = (*color, 58)
+    outline = (*color, 185)
+    for triangle in triangles[order]:
+        polygon = [tuple(map(float, uv[index])) for index in triangle]
+        draw.polygon(polygon, fill=fill, outline=outline, width=1)
+    image.paste(Image.alpha_composite(image.convert("RGBA"), layer).convert("RGB"))
+
+
+def camera_layout(sequence_root: Path, report_root: Path) -> dict:
+    cameras = []
+    for index, view in enumerate(sorted(sequence_root.iterdir())):
+        meta_path = view / "metainfo.json"
+        if not meta_path.is_file():
+            continue
+        meta = load_json(meta_path)
+        extrinsic = load_tensor(view / "camera/extrinsic.pt")
+        transform = extrinsic[0]
+        rotation, translation = transform[:3, :3], transform[:3, 3]
+        center = -rotation.T @ translation
+        forward = rotation.T @ np.array([0.0, 0.0, 1.0], dtype=np.float32)
+        all_centers = -np.einsum("tji,tj->ti", extrinsic[:, :3, :3], extrinsic[:, :3, 3])
+        drift = float(np.linalg.norm(all_centers - center, axis=1).max())
+        cameras.append({
+            "camera_id": meta["camera_id"], "view_type": meta["view_type"],
+            "center": center, "forward": forward / max(np.linalg.norm(forward), 1e-8),
+            "max_center_drift_m": drift, "color": CAMERA_COLORS[index % len(CAMERA_COLORS)],
+        })
+    if not cameras:
+        raise ValueError("no cameras for layout")
+    width, height = 1400, 620
+    canvas = Image.new("RGB", (width, height), "white")
+    draw = ImageDraw.Draw(canvas)
+    panels = [("Top view (world X-Z)", 0, 2), ("Front view (world X-Y)", 0, 1)]
+    centers = np.stack([row["center"] for row in cameras])
+    for panel_index, (title, axis_x, axis_y) in enumerate(panels):
+        left = 35 + panel_index * width // 2
+        right = (panel_index + 1) * width // 2 - 35
+        top, bottom = 55, height - 45
+        values = centers[:, [axis_x, axis_y]]
+        span = max(float(np.ptp(values[:, 0])), float(np.ptp(values[:, 1])), 0.25)
+        center_2d = values.mean(axis=0)
+        scale = min((right - left) / (span * 1.35), (bottom - top) / (span * 1.35))
+        def pixel(value):
+            return (left + (right - left) / 2 + (value[0] - center_2d[0]) * scale,
+                    top + (bottom - top) / 2 - (value[1] - center_2d[1]) * scale)
+        draw.rectangle((left, top, right, bottom), outline=(205, 210, 220), width=2)
+        draw.text((left, 20), title + " - first source frame", fill=(20, 20, 20))
+        arrow_length = span * 0.18
+        for row in cameras:
+            origin = row["center"][[axis_x, axis_y]]
+            direction = row["forward"][[axis_x, axis_y]]
+            norm = np.linalg.norm(direction)
+            tip = origin if norm < 1e-8 else origin + direction / norm * arrow_length
+            p0, p1 = pixel(origin), pixel(tip)
+            draw.line((p0, p1), fill=row["color"], width=5)
+            draw.ellipse((p0[0] - 8, p0[1] - 8, p0[0] + 8, p0[1] + 8), fill=row["color"], outline="black")
+            draw.text((p0[0] + 10, p0[1] - 8), f'{row["camera_id"]} ({row["view_type"]})', fill=row["color"])
+    output = report_root / "camera-layout.png"
+    canvas.save(output)
+    baseline = []
+    for first in cameras:
+        baseline.append({second["camera_id"]: float(np.linalg.norm(first["center"] - second["center"])) for second in cameras})
+    return {
+        "image": output.name,
+        "coordinate_frame": "world",
+        "frame": "first source frame",
+        "cameras": [{k: (v.tolist() if isinstance(v, np.ndarray) else v) for k, v in row.items() if k != "color"} for row in cameras],
+        "baseline_m": {row["camera_id"]: baseline[index] for index, row in enumerate(cameras)},
+    }
+
+
+def render_view(view: Path, report_root: Path, ffmpeg: str, model_dir: Path) -> dict:
+    meta = load_json(view / "metainfo.json")
+    frames = [json.loads(line) for line in (view / "camera/frames.jsonl").read_text(encoding="utf-8").splitlines()]
+    total = meta["num_frames"]
+    if len(frames) != total:
+        raise ValueError(f"frame table length mismatch: {view}")
+    width, height = meta["camera"]["image_size"]
+    intrinsic = load_tensor(view / "camera/intrinsic.pt")
+    motion = load_tensor(view / "annotation/mano/motion.pt")
+    mano_valid = load_tensor(view / "annotation/mano/motion_valid.pt").astype(bool)
+    if intrinsic.shape != (total, 3, 3) or motion.shape != (total, 2, 61) or mano_valid.shape != (total, 2):
+        raise ValueError(f"tensor shape mismatch: {view}")
+    hands = mano_meshes(model_dir, motion, mano_valid)
+
+    objects = []
+    for object_root in sorted((view / "annotation/object").iterdir()):
+        info = load_json(object_root / "metainfo.json")
+        if not info.get("has_model"):
+            continue
+        mesh = view / info["mesh_ref"]
+        vertices, faces = obj_mesh(mesh)
+        pose = load_tensor(object_root / "pose.pt")
+        valid = load_tensor(object_root / "pose_valid.pt").astype(bool)
+        objects.append((info["object_id"], vertices, faces, pose, valid))
+
+    scale = min(1.0, 960.0 / width)
+    shown_width, shown_height = round(width * scale), round(height * scale)
+    relative_video = Path("media") / f"{meta['camera_id']}.mp4"
+    writer = video_writer(ffmpeg, report_root / relative_video, shown_width * 2, shown_height)
+    decoded = 0
+    try:
+        for index, rgb in enumerate(decode_video(ffmpeg, view / "observation/rgb.mkv", width, height)):
+            original = Image.fromarray(rgb)
+            overlay = original.copy()
+            for side_index in range(2):
+                if mano_valid[index, side_index]:
+                    vertices, faces = hands[side_index]
+                    overlay_mesh(overlay, vertices[index], faces, intrinsic[index], HAND_COLORS[side_index])
+            for object_index, (_, vertices, faces, poses, valid) in enumerate(objects):
+                if valid[index] and len(vertices):
+                    camera = vertices @ poses[index, :3, :3].T + poses[index, :3, 3]
+                    overlay_mesh(overlay, camera, faces, intrinsic[index], OBJECT_COLORS[object_index % len(OBJECT_COLORS)])
+            if scale != 1.0:
+                original = original.resize((shown_width, shown_height), Image.Resampling.BILINEAR)
+                overlay = overlay.resize((shown_width, shown_height), Image.Resampling.BILINEAR)
+            panel = Image.new("RGB", (shown_width * 2, shown_height))
+            panel.paste(original, (0, 0))
+            panel.paste(overlay, (shown_width, 0))
+            writer.stdin.write(np.asarray(panel).tobytes())
+            decoded += 1
+        writer.stdin.close()
+        error = writer.stderr.read().decode("utf-8", "replace")
+        code = writer.wait()
+    except Exception:
+        writer.kill()
+        raise
+    if code:
+        raise RuntimeError(error)
+    if decoded != total:
+        raise ValueError(f"decoded {decoded}/{total} frames: {view}")
+    return {
+        "camera_id": meta["camera_id"], "view_type": meta["view_type"], "frames": total,
+        "video": relative_video.as_posix(), "mano_valid": mano_valid.sum(axis=0).tolist(),
+        "objects": [row[0] for row in objects], "source_fps": meta["sampling"]["source_fps"],
+    }
+
+
+def render(args) -> None:
+    release = Path(args.release).resolve()
+    sequence_root = release / "data" / args.sequence
+    if not sequence_root.is_dir():
+        raise FileNotFoundError(sequence_root)
+    report_root = Path(args.output).resolve() if args.output else release / "doc/validation" / args.stage / args.sequence
+    report_root.mkdir(parents=True, exist_ok=True)
+    ffmpeg = find_ffmpeg(args.ffmpeg)
+    model_dir = Path(args.mano_model_dir).resolve()
+    rows = [render_view(view, report_root, ffmpeg, model_dir) for view in sorted(sequence_root.iterdir()) if (view / "metainfo.json").is_file()]
+    if not rows:
+        raise ValueError("no camera views")
+    layout = camera_layout(sequence_root, report_root)
+    camera_ids = [row["camera_id"] for row in rows]
+    baseline_header = "".join(f"<th>{camera_id}</th>" for camera_id in camera_ids)
+    baseline_rows = "".join(
+        f'<tr><th>{camera_id}</th>' + "".join(f'<td>{layout["baseline_m"][camera_id][other]:.3f}</td>' for other in camera_ids) + "</tr>"
+        for camera_id in camera_ids
+    )
+    drift_rows = "".join(
+        f'<tr><th>{row["camera_id"]}</th><td>{row["view_type"]}</td><td>{row["max_center_drift_m"]:.3f}</td></tr>'
+        for row in layout["cameras"]
+    )
+    cards = "".join(
+        f'<section><h2>{row["camera_id"]} · {row["view_type"]}</h2>'
+        f'<video controls preload="metadata" src="{row["video"]}"></video>'
+        f'<p>全部 {row["frames"]} 帧，按官方采集速度 30 FPS 播放。左栏原图；右栏为 MANO 与物体几何投影。'
+        f'MANO valid 左/右={row["mano_valid"]}；物体={", ".join(row["objects"]) or "无"}。</p></section>'
+        for row in rows
+    )
+    html = f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width">
+<title>OakInk2 {args.sequence} 校验</title><style>body{{font:16px/1.6 system-ui;max-width:1500px;margin:auto;padding:24px;background:#f5f6f8}}header,section{{background:white;padding:20px;margin:16px 0;border-radius:12px}}video,img{{width:100%;height:auto}}table{{border-collapse:collapse;overflow:auto;display:block}}th,td{{padding:6px 10px;border:1px solid #ccd1da;text-align:right}}.pending{{color:#9a5b00;font-weight:bold}}</style>
+<header><h1>OakInk2 · {args.sequence}</h1><p class="pending">自动校验完成；人工审核待确认。</p>
+<p>所有选中相机和全部帧均已渲染。源中没有 depth 或像素 mask，因此报告不展示这些模态；物体模型来自官方 object_repair。</p>
+<p>右栏手部使用完整 MANO 三角网格；物体使用 OBJ 全部三角面，而非稀疏顶点。颜色仅用于区分几何实体。</p>
+<p>官方论文说明四路 RGB 同步采集为 30 FPS；本报告按 30 FPS 播放。共享 MKV 的 1 FPS 仅是索引封装时间基，不代表采集速度；帧对齐仍以原始 frame_id 和 frames.jsonl 为准。</p></header>
+<section><h2>多相机相对位置</h2><img src="{layout["image"]}" alt="camera layout"><p>由首个源帧的 <code>T_camera_from_world</code> 反算相机中心和朝向；坐标为世界坐标，距离单位为米。</p>
+<table><thead><tr><th>baseline (m)</th>{baseline_header}</tr></thead><tbody>{baseline_rows}</tbody></table>
+<h3>序列内相机中心最大位移</h3><table><thead><tr><th>camera</th><th>view</th><th>max drift (m)</th></tr></thead><tbody>{drift_rows}</tbody></table>
+<p>外部固定相机的数值误差接近零；ego 相机会随头部运动，因此首帧布局不能代表整段中的唯一位置。</p></section>{cards}</html>'''
+    (report_root / "index.html").write_text(html, encoding="utf-8")
+    write_json(report_root / "report.json", {"dataset_id": "OakInk2", "sequence": args.sequence, "views": rows, "camera_layout": layout, "status": "automatic_checks_passed"})
+    write_json(report_root / "sampling.json", {"method": "complete_sequence", "sequence": args.sequence, "all_cameras": True, "all_frames": True})
+    write_json(report_root / "human-review.json", {
+        "dataset_id": "OakInk2", "release_id": "shared-v1.0", "stage": args.stage,
+        "status": "pending", "review_scope": "complete selected sequence, all cameras, all frames and available geometric modalities",
+        "reviewed_report": "index.html", "reviewed_report_sha256": None,
+        "confirmed_at": None, "confirmed_by": None,
+        "notes": "Automatic checks do not constitute human approval.",
+    })
+    print(report_root / "index.html")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--release", required=True)
+    parser.add_argument("--sequence", required=True)
+    parser.add_argument("--mano-model-dir", required=True)
+    parser.add_argument("--ffmpeg")
+    parser.add_argument("--output", help="Optional report directory; useful for atomic review regeneration")
+    parser.add_argument("--stage", choices=("pilot", "full_random"), default="pilot")
+    render(parser.parse_args())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/OakInk-v2/shared-v1.0/script/requirements-lock.txt b/OakInk-v2/shared-v1.0/script/requirements-lock.txt
new file mode 100644
index 0000000000000000000000000000000000000000..87a6d5ddcc9c8b16fb9d54f847601be9065b98fb
--- /dev/null
+++ b/OakInk-v2/shared-v1.0/script/requirements-lock.txt
@@ -0,0 +1,6 @@
+torch==2.14.0+cpu
+numpy==2.5.3
+scipy==1.18.1
+Pillow==12.3.0
+imageio-ffmpeg==0.6.0
+smplx==0.1.28