| --- |
| 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} |
| } |
| ``` |
