README.md

metadata

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 Space.

Contents

samples/
├── index.json                 # browse-time episode list (consumed by the Space)
└── <clip_id>/
    ├── scene.json             # camera intrinsics, object metadata, durations
    ├── signals.json           # per-frame action signals (timeseries)
    ├── thumb.jpg              # 320×180 preview rendered from depth
    ├── depth.mp4              # MoGe-2 depth, inferno colormap (854×480)
    ├── flow.mp4               # MEMFOF optical flow visualization
    ├── mask.mp4               # SAM-tracked object mask cutout
    ├── recording.viser        # full 3D scene (point cloud + meshes + hands)
    ├── 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

<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) 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

@misc{egoinfinity2026,
  title  = {EgoInfinity: TBD},
  author = {Rice Robot Perception \& Intelligence Lab},
  year   = {2026},
  note   = {Preview release}
}