|
|
--- |
|
|
language: |
|
|
- en |
|
|
pretty_name: PAI-Bench-Transfer |
|
|
configs: |
|
|
- config_name: benchmark |
|
|
data_files: |
|
|
- split: PAIBenchTransfer |
|
|
path: metadata.csv |
|
|
task_categories: |
|
|
- video-to-video |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
# Physical AI Bench - Conditional Generation |
|
|
|
|
|
[Paper](https://huggingface.co/papers/2512.01989) | [Code](https://github.com/SHI-Labs/physical-ai-bench) |
|
|
|
|
|
This dataset (Phsical AI benchmark, PAI-Bench) consisting of 600 examples across three key scenarios: robotic arm operations, driving, and ego-centric everyday life scenes, each representing a critical aspect of Physical AI. This dataset is constructed by sampling a number of videos from three different datasets. The specific details are provided below. |
|
|
|
|
|
| Dataset | Category | Sample Nums | |
|
|
| ------------------------------------------------------------ | ------------------ | ----------- | |
|
|
| [Agibot World](https://github.com/OpenDriveLab/AgiBot-World) | Robotics | 200 | |
|
|
| [OpenDV](https://github.com/OpenDriveLab/DriveAGI.git) | Autonomous Driving | 200 | |
|
|
| [Ego-Exo4D](https://ego-exo4d-data.org/) | Ego-centric | 200 | |
|
|
|
|
|
## Dataset Summary |
|
|
|
|
|
- **Dataset Size**: 600 video samples |
|
|
- **Video Format**: MP4 files with various processing variants |
|
|
- **Annotations**: Text captions for each video |
|
|
- **Processing Variants**: Blur, Canny edge detection, Depth estimation, SAM2 segmentation |
|
|
|
|
|
## File Organization |
|
|
|
|
|
```text |
|
|
physical-ai-bench-transfer/ |
|
|
├── videos/ # Original video files |
|
|
├── blur/ # Blur-processed videos |
|
|
├── canny/ # Edge detection videos |
|
|
├── depth_vids/ # Depth estimation videos |
|
|
├── depth_npzs/ # Depth estimation numpy arrays |
|
|
├── sam2_vids/ # SAM2 segmentation videos |
|
|
├── sam2_pkls/ # SAM2 segmentation pickle files |
|
|
└── captions/ # JSON files with video descriptions |
|
|
``` |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use Physical AI Bench in your research, please cite: |
|
|
|
|
|
```bibtex |
|
|
@misc{zhou2025paibenchcomprehensivebenchmarkphysical, |
|
|
title={PAI-Bench: A Comprehensive Benchmark For Physical AI}, |
|
|
author={Fengzhe Zhou and Jiannan Huang and Jialuo Li and Deva Ramanan and Humphrey Shi}, |
|
|
year={2025}, |
|
|
eprint={2512.01989}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CV}, |
|
|
url={https://arxiv.org/abs/2512.01989}, |
|
|
} |
|
|
``` |
