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

# AFUN_eval

The three 3D-motion evaluation sets of **AFUN**
([arXiv:2606.02551](https://arxiv.org/abs/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**](https://huggingface.co/datasets/AFUN-dataset/AFUN);
these evaluation sets are disjoint from it at the sample level.

## Download

```bash
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}, ...]`

```python
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:

```python
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

```bibtex
@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}
}
```