README.md

---
base_model:
- Wan-AI/Wan2.1-T2V-14B
datasets:
- BestWishYsh/OpenS2V-Eval
- BestWishYsh/OpenS2V-5M
language:
- en
license: apache-2.0
pipeline_tag: text-to-video
library_name: diffusers
---

<div align=center>
<img src="https://github.com/PKU-YuanGroup/OpenS2V-Nexus/blob/main/__assets__/OpenS2V-Nexus_logo.png?raw=true" width="300px">
</div>
<h2 align="center"> <a href="https://pku-yuangroup.github.io/OpenS2V-Nexus/">OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation</a></h2>

<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.  </h5>

## ✨ Summary
1. **New S2V Benchmark.** 
   - We introduce *OpenS2V-Eval* for comprehensive evaluation of S2V models and propose three new automatic metrics aligned with human perception.
2. **New Insights for S2V Model Selection.**
   - Our evaluations using *OpenS2V-Eval* provide crucial insights into the strengths and weaknesses of various subject-to-video generation models.
3. **Million-Scale S2V Dataset.** 
   - We create *OpenS2V-5M*, a dataset with 5.1M high-quality regular data and 0.35M Nexus Data, the latter is expected to address the three core challenges of subject-to-video.

## 💡 Description
- **Repository:** [Code](https://github.com/PKU-YuanGroup/OpenS2V-Nexus), [Page](https://pku-yuangroup.github.io/OpenS2V-Nexus/), [Dataset](https://huggingface.co/datasets/BestWishYsh/OpenS2V-5M), [Benchmark](https://huggingface.co/datasets/BestWishYsh/OpenS2V-Eval)
- **Paper:** [https://huggingface.co/papers/2505.20292](https://huggingface.co/papers/2505.20292)
- **Point of Contact:** [Shenghai Yuan](shyuan-cs@hotmail.com)

## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star and citation.

```BibTeX
@article{yuan2025opens2v,
  title={OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation},
  author={Yuan, Shenghai and He, Xianyi and Deng, Yufan and Ye, Yang and Huang, Jinfa and Lin, Bin and Luo, Jiebo and Yuan, Li},
  journal={arXiv preprint arXiv:2505.20292},
  year={2025}
}
```