--- title: OpenS2V Eval emoji: 📊 colorFrom: gray colorTo: blue sdk: gradio sdk_version: 5.31.0 app_file: app.py pinned: false license: apache-2.0 short_description: A Detailed Benchmark for Subject-to-Video Generation thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/63468720dd6d90d82ccf3450/N9kKR052363-MYkJkmD2V.png ---

OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

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## ✨ Summary **OpenS2V-Eval** introduces 180 prompts from seven major categories of S2V, which incorporate both real and synthetic test data. Furthermore, to accurately align human preferences with S2V benchmarks, we propose three automatic metrics: **NexusScore**, **NaturalScore**, **GmeScore** to separately quantify subject consistency, naturalness, and text relevance in generated videos. Building on this, we conduct a comprehensive evaluation of 14 representative S2V models, highlighting their strengths and weaknesses across different content. ## 📣 Evaluate Your Own Models For how to evaluate your customized model like OpenS2V-Eval in the [OpenS2V-Nexus paper](https://huggingface.co/papers/2505.20292), please refer to [here](https://github.com/PKU-YuanGroup/OpenS2V-Nexus/tree/main/eval). ## ⚙️ Get Videos Generated by Different S2V models For more details, please refer to [here](https://huggingface.co/datasets/BestWishYsh/OpenS2V-Eval/tree/main/Results). ## 💡 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} } ```