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| license: mit |
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| # Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| Here is the training checkpoints of **[Diff-V2M (AAAI'26)](https://arxiv.org/abs/2511.09090)** |
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| ## Overview |
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| Diff-V2M is a hierarchical diffusion model with explicit rhythmic modeling and multi-view feature conditioning, achieving state-of-the-art results in video-to-music generation.. |
| <img src="model.jpg" width="770" height="400" alt="model"/> |
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| ## Model Sources |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** https://github.com/Tayjsl97/Diff-V2M |
| - **Demo:** [demo page](https://tayjsl97.github.io/Diff-V2M-Demo) |
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| ## Citation |
| If you use our models in your research, please cite it as follows: |
| ```bib |
| @inproceedings{ji2026diff, |
| title={Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation}, |
| author={Ji, Shulei and Wang, Zihao and Yu, Jiaxing and Yang, Xiangyuan and Li, Shuyu and Wu, Songruoyao and Zhang, Kejun}, |
| booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, |
| year={2026} |
| } |
