README.md

metadata

title: EgoInfinity Browser
emoji: 🪞
colorFrom: indigo
colorTo: pink
sdk: static
pinned: false
license: mit
short_description: Static viewer for the EgoInfinity dataset

EgoInfinity Browser

A static, browser-only viewer for curated EgoInfinity clips. No backend, no Python, no live viser server. Every clip is served as pre-baked assets and rendered with three.js / the viser-build-client.

Source code: https://github.com/Rice-RobotPI-Lab/EgoInfinity

Dataset: https://huggingface.co/datasets/Rice-RobotPI-Lab/egoinfinity

What you see

For each clip:

• YouTube iframe: the original source video, embedded from YouTube.
• Depth video (MoGe-2) and optical flow (MEMFOF), rendered as colormapped MP4s.
• SAM-tracked object mask highlighting the active object across the clip.
• 3D scene: viser recording showing the per-frame point cloud, hand mesh, object meshes (Gaussian Splat baked), and skeleton.
• Hand skeleton overlay projected onto the depth video.
• Action signals: per-frame timeseries (hand velocity, contact, etc.).

Get the data

The full processed dataset is hosted on Hugging Face Datasets:

# Snapshot download (entire dataset, ~370 MB)
pip install huggingface_hub
hf download Rice-RobotPI-Lab/egoinfinity --repo-type=dataset --local-dir ./egoinfinity-data

# Or browse interactively at:
# https://huggingface.co/datasets/Rice-RobotPI-Lab/egoinfinity

While this Space is private, you must be logged into a Hugging Face account with read access to both repositories for assets to load in the viewer.

License & attribution

• Code in this Space (HTML, JS, viser-client) is released under the MIT License. See LICENSE.
• Data assets (depth/flow/mask videos, viser recordings, hand parameters, scene metadata) are derivative works of Action100M (Meta FAIR) and are governed by the FAIR Noncommercial Research License v1. See LICENSE-Action100M.
• Original videos remain on YouTube and are embedded via the YouTube IFrame API. Only the SAM-tracked region of each frame appears in the derivative mask.mp4 (the rest is painted black).
• Hand visualization uses pose parameters derived from the MANO hand model (Romero et al., 2017). MANO is not redistributed; only baked vertex positions appear in the viewer.
• WiLoR weights (CC-BY-NC-ND) are not redistributed.

Citation

If you find this useful in your research, please cite:

@misc{egoinfinity2026,
  title  = {EgoInfinity: A Web-Scale Data Engine for Video-to-Action Robot Learning through Egocentric Views},
  author = {Rice RobotPI Lab},
  year   = {2026},
  note   = {Preview release}
}