File size: 3,770 Bytes
8796997 3f17c6e 8796997 be06c17 9d731e1 be06c17 9d731e1 be06c17 9d731e1 be06c17 9d731e1 be06c17 9d731e1 4daf74a 8796997 4cff041 8796997 60a23b7 8796997 4cff041 8796997 4cff041 8796997 b2475c4 4cff041 8796997 4cff041 4daf74a 8796997 4daf74a 8796997 4cff041 8796997 4cff041 8796997 4cff041 4aa6d05 4cff041 8796997 4cff041 8796997 3f17c6e 8796997 b640c34 8796997 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | ---
license: mit
language:
- en
size_categories:
- n<1K
tags:
- benchmark
- world-model
- video-generation
- programmable-world-models
arxiv: "2610.02205"
pretty_name: PROWBench — Episodes
configs:
- config_name: default
default: true
data_files:
- split: full
path: splits/full.parquet
- split: verified_first_frame
path: splits/verified_first_frame.parquet
- split: unverified_first_frame
path: splits/unverified_first_frame.parquet
- split: long_video
path: splits/long_video.parquet
- split: multi_view
path: splits/multi_view.parquet
- split: gunfight
path: splits/gunfight.parquet
---
# PROWBench Dataset
PROWBench evaluates whether video models follow program-specified motion and interactions. It contains **170 episodes, 200 camera views, and 600 proxy videos**, with prompts, recorded world states, and RGB first frames where available.
[Paper](https://arxiv.org/abs/2610.02205) · [Project](https://alaya-lab.github.io/PROWBench/) · [Gallery](https://alaya-lab.github.io/PROWBench/gallery.html) · [Code](https://github.com/AlayaLab/PROWBench) · [Evaluation guide](https://github.com/AlayaLab/PROWBench/blob/main/eval/USAGE.md)
## Download
```bash
pip install -U huggingface_hub
hf download AlayaLab/PROWBench --repo-type dataset --local-dir PROWBench
```
Load sample metadata and preview images in Python (`pip install -U datasets pillow`):
```python
from datasets import load_dataset
dataset = load_dataset("AlayaLab/PROWBench")
sample = dataset["full"][0]
```
The `*_file` fields contain paths relative to the dataset root. Use the download command above for the full videos, prompts and engine records.
## Data
| Split | Episodes | Duration | RGB first frames |
|---|---:|---:|---:|
| `verified_first_frame` | 40 | 5 s | 40 |
| `unverified_first_frame` | 90 | 5 s | 0 |
| `long_video` | 10 | 30 s | 0 |
| `multi_view` | 10 | 5 s | 0 |
| `gunfight` | 20 | 5 s | 20 |
Each view has three proxy representations: `white` (coarse 3D), `mixed` (semantic proxy), and `colored-obb` (3D boxes). Multi-view episodes have four synchronized cameras.
The `full` Viewer split contains **600 rows**, one per camera–representation pair; the other splits are subsets. Full prompts are available through `prompt_file`. `preview_kind` distinguishes RGB first-frame references from proxy previews.
A typical episode:
```text
episodes/<group>/<episode>/
├── manifest.json
├── scene/ # Recorded world states
├── cameras/ # Camera records
└── views/<camera>/
├── <representation>/video.mp4
├── <representation>/prompt.txt
├── prompt-context/first_frame.png # When available
└── visibility.json # Multi-view
```
Long-video prompts use JSON files or a `prompts/` directory of windowed prompts. Use `prompt_file` and the adjacent `sample.json` for exact input paths, including versioned episodes.
This release contains conditioning inputs and annotations; raw depth maps and generated model outputs are not included. [Release metadata](metadata/release.json) · [Sample metadata](metadata/samples.jsonl.gz).
## License
This dataset is released under the [MIT License](LICENSE).
## Citation
If you find PROWBench helpful for your research, please consider citing our paper:
```bibtex
@misc{huang2026prowbench,
title={PROWBench: Do Video Models Render What the Program Specifies?},
author={Zheng-Hui Huang and Guixu Lin and Yu-Ju Tsai and Jian-Kai Zhu and Fengbo Lan and Yu-Lun Liu and Yung-Yu Chuang and Kaipeng Zhang and Zhixiang Wang},
year={2026},
eprint={2610.02205},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2610.02205}
}
```
|