---
license: cc-by-nc-sa-4.0
task_categories:
  - video-classification
  - robotics
tags:
  - world-model
  - evaluation
  - embodied-ai
  - ground-truth
pretty_name: "WM-Eval GT: EWMBench Ground Truth"
size_categories:
  - 1K<n<10K
---

# WM-Eval GT: EWMBench Ground Truth

Ground truth data for [EWMBench](https://github.com/AgibotTech/EWMBench) evaluation, extracted and restructured for direct use by the [wm-evaluation-harness](https://github.com/Physis-AI/wm-evaluation-harness) framework.

## Source

Extracted from [`agibot-world/EWMBench`](https://huggingface.co/datasets/agibot-world/EWMBench) (`gt_dataset.tar`). Original dataset by [AgibotTech](https://github.com/AgibotTech).

## Dataset Structure

```
{task_id}/
├── {episode_id}/
│   ├── prompt/
│   │   ├── init_frame.png      # Initial frame for generation
│   │   └── prompt.txt          # Task description
│   └── video/
│       ├── frame_00000.jpg     # Ground truth frame sequence
│       ├── frame_00001.jpg
│       └── ...
```

## Statistics

| Item | Count |
|------|-------|
| Tasks | 7 (367, 392, 497, 511, 543, 558, 574) |
| Episodes per task | 3 |
| Total episodes | 21 |
| Total files | 3,959 |
| Size | ~129 MB |

## Usage with wm-evaluation-harness

This dataset is auto-downloaded by the framework when referenced as a HuggingFace dataset repo ID:

```yaml
# configs/ewmbench_hf.yaml
evaluation:
  gt_dir: Physis-AI/wm-eval-gt-ewmbench    # auto-download
  benchmarks:
    ewmbench:
      metrics: [psnr, ssim]
```

```bash
wm-eval evaluate --config configs/ewmbench_hf.yaml --videos <your_pred_dir>
```

Or used programmatically:

```python
from wm_eval.evaluation.models.registry import ModelRegistry

registry = ModelRegistry()
gt_path = registry.resolve("Physis-AI/wm-eval-gt-ewmbench", repo_type="dataset")
```

## License

This dataset follows the original [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license from AgibotTech/EWMBench.

## Citation

```bibtex
@article{ewmbench2025,
  title={EWMBench: Evaluating Scene Understanding and Generation Quality for Embodied World Models},
  author={AgibotTech},
  year={2025},
  eprint={2505.09694},
  archivePrefix={arXiv}
}
```