Physion-Eval / README.md
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metadata
license: other
task_categories:
  - text-to-video
  - image-to-video
language:
  - en
tags:
  - video
  - multimodal
  - physical-reasoning
  - evaluation
  - synthetic-data
pretty_name: Physion-Processed Video Dataset

🎬 PHYSION-EVAL: The First Human-Centered Benchmark for Physical Realism in AI-Generated Videos

✨ Overview

This dataset is developed by Physion Labs, a research team focused on advancing physical realism and reliability in multimodal generative AI.

We created this dataset to support physically grounded video generation, moving beyond visual realism toward true physical consistency. It enables research in:

  • Perceptual physical realism evaluation for AI-generated videos
  • Temporal consistency and causal reasoning
  • Human vs. model perception of physical plausibility

By identifying where current models break physical rules, we aim to enable more reliable and trustworthy generative video systems.

🛡️ Content Filtering

In this open-source version, we apply filtering procedures to reduce privacy and intellectual property risks:

  • 🚫 Videos with identifiable human faces are excluded
  • 🚫 Videos with logos, brand marks, or trademarks are excluded

Permitted Use

This dataset is intended to support:

  • Academic and non-commercial research.
  • Benchmarking and evaluation of video generation models.
  • Studying physical realism and perceptual consistency in AI-generated videos.

This dataset must not be used for:

  • Surveillance or biometric identification.
  • Any application that violates privacy, publicity, or intellectual property rights.
  • High-risk or safety-critical decision-making systems.
  • Training, fine-tuning, or distillation of generative models without explicit permission

Citation

If you use this dataset, please cite:

@misc{zhang2026physionevalevaluatingphysicalrealism,
      title={Physion-Eval: Evaluating Physical Realism in Generated Video via Human Reasoning}, 
      author={Qin Zhang and Peiyu Jing and Hong-Xing Yu and Fangqiang Ding and Fan Nie and Weimin Wang and Yilun Du and James Zou and Jiajun Wu and Bing Shuai},
      year={2026},
      eprint={2603.19607},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.19607}, 
}

📜 License

Custom Research Use Only License

By using this dataset, you agree to:

  • Use it for non-commercial research and evaluation purposes only
  • Not redistribute the dataset in any way that violates applicable laws or third-party rights
  • Comply with all relevant laws, regulations, and ethical guidelines