---
pretty_name: WorldOracle Validation Ground Truth
---

# WorldOracle Validation Ground Truth

This public dataset contains the frozen WorldOracle validation split and its
ground-truth data. It contains validation episodes only and does not include
test-set ground truth.

The data is stored as one uncompressed tar archive per episode. This keeps the
repository resumable and avoids hundreds of thousands of individual Hub files.
Extract an episode with `tar -xf <episode>.tar -C <destination>`.

## Platforms

| Platform | Episodes |
| --- | ---: |
| AgiBot | 20 |
| YAM | 15 |
| Human Hand | 20 |
| Policy | 12 |
| **Total** | **67** |

`validation_manifest.json` is the canonical inventory. It records the path,
size and SHA-256 of every episode archive. `SHA256SUMS` covers all repository
files except itself. Validation inputs are available under `inputs/`.

## Human Hand absolute pose format

Human Hand validation inputs provide synchronized absolute hand and camera
poses in `controls/hand_camera_poses.npz`. Pose sample `t` corresponds directly
to validation video frame `t`.

- `left_positions_camera` and `right_positions_camera` contain the 21 hand
  joints in the released camera-reference coordinates.
- `left_quaternions_camera_xyzw` and `right_quaternions_camera_xyzw` contain
  the corresponding joint orientations in XYZW order.
- `camera_position_world` and `camera_quaternion_world_xyzw` contain the
  tracked camera-reference pose.
- `left_valid`, `right_valid`, and `camera_valid` indicate whether each
  measurement is usable.

The leading initialization interval in which the pose tracking was invalid has
been removed, so the first released sample is valid. Any unavailable
measurements occurring later in an episode remain present and are marked by
the validity arrays. Delta-action tensors, source-frame indices, internal
source mappings, and cleaning records are not part of the public release.

## Human Hand joint visualization

[`scripts/visualize_human_hand.py`](scripts/visualize_human_hand.py) provides a
standalone example for projecting the released 3D hand joints onto the
rectified `top_head` frames of a public Human Hand validation episode. It reads
the episode's released `hand_camera_poses.npz` and `calibration.json`, applies
the tracked-camera, physical-fisheye, and virtual-camera transformations, and
renders the left- and right-hand 21-joint skeletons next to the original
frames.

Install the two runtime dependencies with:

```bash
python -m pip install numpy opencv-python
```

After extracting one of the public Human Hand validation archives, run:

```bash
python scripts/visualize_human_hand.py \
  --npz inputs/human_hand/<task>/<episode>/controls/hand_camera_poses.npz \
  --calibration inputs/human_hand/<task>/<episode>/calibration.json \
  --frames-dir <extracted-episode>/video/top_head \
  --output hand_joint_projection.mp4 \
  --fps 15
```

The script requires the image sequence and NPZ arrays to have identical frame
counts and respects the released hand- and camera-validity masks. It contains
no hard-coded episode list or camera parameters; all geometry is read from the
selected public validation episode.

## License and Citation

All data in this repository are licensed under [**CC BY-NC-SA 4.0**](https://creativecommons.org/licenses/by-nc-sa/4.0/). If you find our benchmark useful in your research, please consider citing our work.

```bibtex
@article{duan2026worldoracle,
  title={WorldOracle: Benchmarking Action-Conditioned World Models against Real-World Executions},
  author={Duan, Haoyi and Chen, Liliang and Wang, Nan and Zhang, Yang and Lu, Yuhang and Chen, Xiaotong and Tseng, Wei-Cheng and Gao, Shenyuan and Li, Zhaoshuo and Jin, Jingyi and others},
  journal={arXiv preprint arXiv:2610.18826},
  year={2026}
}