AFUN_eval

The three 3D-motion evaluation sets of AFUN (arXiv:2606.02551, Table 3).

subset	folder	samples	description
AFUN test	afun_test/	121	held-out split across six robot/human sources
SceneFun3D test	scenefun3d_test/	721	from the original SceneFun3D validation visits
RoboMIND2 test	robomind2_test/	156	out-of-domain robot data

The training data is released separately as AFUN; these evaluation sets are disjoint from it at the sample level.

Download

pip install -U huggingface_hub
hf download AFUN-dataset/AFUN_eval --repo-type dataset --local-dir afun_eval

Layout

Each subset has a manifest.json (one entry per sample: path, source fields, and language — the task instruction) plus one folder per sample:

<subset>/<source>/<episode>/<interval>/<cam>/
├── obs_frame.png                    # RGB frame
├── obs_frame_depth.npy              # float32 H×W depth, millimeters
├── sam_mask.png                     # GT affordance mask (non-zero = functional region)
└── trajectory.json                  # GT 3D motion + camera intrinsics

trajectory.json

3D positions are in the camera frame, in meters. Every file has the two fields the evaluation reads:

• camera_info — intrinsics (fx, fy, cx, cy), distortion, T_base_to_cam
• trajectory_3d — ground-truth motion of the interaction point: [{frame_idx, position_3d}, ...]

traj = json.load(open(f"{s['path']}/trajectory.json"))
gt = [p["position_3d"] for p in traj["trajectory_3d"]]   # camera frame, meters
fx = traj["camera_info"]["intrinsics"]["fx"]

afun_test / robomind2_test files also carry motion_2d (start/end pixel) and the fitted spline_params; the evaluation protocol does not read them.

Evaluation protocol (paper §5.2.3)

The method receives obs_frame.png, obs_frame_depth.npy, the camera intrinsics, and the instruction (from manifest.json), and predicts a 3D motion curve in the camera frame.

• Prediction and ground-truth trajectory_3d are linearly resampled to 50 points each.
• ADE / FDE — mean / final-point L2 distance in meters, computed in absolute scale (subscript a) and in relative scale (subscript r: both curves shifted so their first point is the origin).
• CIM (contact-in-mask) — the first point of the predicted curve, pinhole-projected to pixels with the intrinsics, must land inside the GT mask (sam_mask.png > 127); projecting outside the image is a miss. Reported as the hit rate over all samples; samples where the method produced no prediction count as misses.
• #fail — number of samples the method could not produce a prediction for.

Reproducing the paper numbers

The instruction lives in manifest.json, so a loader needs to join it with the sample folder. Minimal example:

import json, pathlib
sub = pathlib.Path("afun_eval/afun_test")
for s in json.load(open(sub / "manifest.json"))["samples"]:
    cam = sub / s["path"]
    instruction = s["language"]
    # cam/obs_frame.png, cam/obs_frame_depth.npy, cam/sam_mask.png, cam/trajectory.json

Evaluated with the paper's checkpoint, a fresh download of this release reproduces Table 3:

subset	ADE_a	FDE_a	ADE_r	FDE_r	CIM %	#fail
AFUN test (121)	0.098	0.139	0.080	0.135	81.0	0
SceneFun3D test (721)	0.351	0.441	0.135	0.260	67.3	1
RoboMIND2 test (156)	0.254	0.323	0.177	0.276	62.2	0

Measured on a fresh hf download of this dataset, every metric lands within 0.005 m (and CIM within 0.7 points) of the table above; the residual is bfloat16 inference non-determinism.

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}
}