Datasets:

AFUN-dataset
/

AFUN_pool

pretty_name: AFUN_pool
license: cc-by-nc-sa-4.0
language:
  - en
size_categories:
  - 100K<n<1M
tags:
  - robotics
  - affordance
  - manipulation
  - 3d-motion
  - segmentation

AFUN_pool

The full data pool of AFUN (arXiv:2606.02551): 183,657 data points for affordance segmentation and 3D interaction-motion prediction, i.e. the fitted-curve pool of Table 7 from which the curated AFUN training set (44,749) was selected. AFUN ⊂ AFUN_pool. Same folder format as AFUN, same trajectory.json schemas.

It comes in two parts:

part	data points	what each folder contains
full	77,432	`obs_frame.png`, `obs_frame_depth.npy`, `sam_mask.png`, `trajectory.json`
ego4d (annotations only)	106,225	`sam_mask.png`, `trajectory.json`, `provenance.json` — RGB/depth are rebuilt from your own Ego4D download, see below

The 106,225 ego4d data points are human videos from Ego4D, whose license allows redistributing annotations but not the video frames themselves. Everything we produced for them is here; the frame is one script call away.

Of the 223,334 fitted curves in Table 7, 39,677 are not in this release: their affordance mask was not tracked at the observation frame, or their trajectory.json lacks motion_2d (HOI4D).

Download & extract

pip install -U huggingface_hub
hf download AFUN-dataset/AFUN_pool --repo-type dataset --local-dir afun_pool
cd afun_pool
for f in data/*.tar.zst; do tar --zstd -xf "$f"; done

Download ≈ 363 GiB (of which the ego4d part is 0.26 GiB), extracted ≈ 748 GiB. After extraction:

afun_pool/
├── manifest.json                          # index — one entry per data point, with `part`
├── reconstruct_ego4d_frames.py            # rebuilds obs_frame.png for the ego4d part
├── <source>/<episode>/<interval>/<cam>/   # 77,432 folders  (part = "full")
│   ├── obs_frame.png                      # RGB frame
│   ├── obs_frame_depth.npy                # float32 H×W depth, millimeters
│   ├── sam_mask.png                       # affordance mask (non-zero = actionable region)
│   └── trajectory.json                    # 3D motion + camera intrinsics
└── ego4d/<episode>/<interval>/<cam>/      # 106,225 folders (part = "ego4d")
    ├── sam_mask.png
    ├── trajectory.json
    └── provenance.json                    # which Ego4D frame this is (+ pixel hash)

Loading a data point is identical to AFUN — manifest.json entries carry path, dataset / episode_id / interval / cam, language, shard, plus part. trajectory.json follows the two AFUN schemas (top-level fields for robot / human sources, nested trajectories[] for scenefun3d); see the AFUN README.

Rebuilding the Ego4D frames

Each ego4d folder's provenance.json records the Ego4D video and frame:

{
  "ego4d_video_uid": "0031d268-818c-4ec4-a804-935be610a61a",
  "ego4d_frame_index": 56979,           // 0-based frame in the full_scale video
  "fps": 30.0,
  "image_hw": [1440, 1920],
  "rgb_sha256": "b22fda36…",            // sha256 of the raw H×W×3 uint8 pixels
  "language": "Spread adhesive with the trowel over the underlayment.",
  "vitra_episode_file": "ego4d_other/episodic_annotations/Ego4D_0031d268-…_ep_000859.npy",
  "episode_frame_index": 0,             // index within that VITRA-1M episode
  "depth": { "model": "depth-anything/DA3NESTED-GIANT-LARGE-1.1", "...": "..." }
}

Get Ego4D access at https://ego4d-data.org and download only the videos you need:

pip install ego4d av Pillow
python reconstruct_ego4d_frames.py --pool-root ego4d --list-uids > uids.txt
ego4d --output_directory ~/ego4d --datasets full_scale --version v2 --video_uid_file uids.txt

Decode the frames into place and verify them against the recorded pixel hashes:
```
python reconstruct_ego4d_frames.py --pool-root ego4d --ego4d-root ~/ego4d --verify
```
This writes obs_frame.png into every ego4d/... folder using the same decoder and frame indexing the annotations were made with (PyAV, native resolution, no resizing); --verify confirms each decoded frame matches rgb_sha256.

The intervals were taken from VITRA-1M (MIT), and vitra_episode_file / episode_frame_index locate the same frame in its episode files. Depth maps are not shipped for this part; provenance.json → depth gives the settings we used (Depth Anything 3 streaming video depth, every 4th frame, metric millimeters) if you want to regenerate them.

Sources

key	dataset	part	data points
ego4d	Ego4D via VITRA-1M (human videos)	ego4d	106,225
scenefun3d	SceneFun3D	full	49,706
vitra_epic	EPIC-KITCHENS via VITRA-1M (human videos)	full	9,155
robomind	RoboMIND	full	7,077
calvin	CALVIN	full	3,105
droid	DROID	full	2,751
robomind2	RoboMIND 2	full	2,356
rh20t_human	RH20T human demos	full	1,422
rh20t	RH20T	full	1,044
agibot	AgiBot World	full	783
rlbench	RLBench	full	33

AFUN's vitra source corresponds to vitra_epic ∪ ego4d here. EPIC-KITCHENS frames are redistributed under CC BY-NC 4.0 (Damen et al.); Ego4D frames are not redistributed. The evaluation sets (AFUN_eval) are disjoint from this pool at the sample level.

Citation

@article{wang2026afun,
  title   = {AFUN: Towards an Affordance Foundation Model for Functionality Understanding},
  author  = {Wang, Zhaoning and Zhong, Yi and Fu, Jiawei and Christensen, Henrik I. and Gao, Jun},
  journal = {arXiv preprint arXiv:2606.02551},
  year    = {2026}
}