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