Original model link: https://huggingface.co/Ruihang/Wan-Move-14B-480P

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Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance [Paper]
Ruihang Chu, Yefei He, Zhekai Chen, Shiwei Zhang, Xiaogang Xu, Bin Xia, Dingdong Wang, Hongwei Yi, Xihui Liu, Hengshuang Zhao, Yu Liu, Yingya Zhang, Yujiu Yang

Wan-Move spports diverse motion control applications in image-to-video generation. The generated samples (832×480p, 5s) exhibits high visual fidelity and accurate motion.

The framework of Wan-Move. (a) How to inject motion guidance. (b) Training pipeline.

The contruction pipeline and statistics of MoveBench. Welcome everyone to use it!

Qualitative comparisons between Wan-Move and academic methods and commercial solutions.

If you find our work helpful, please cite us.

@article{chu2025wan,
      title={Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance},
      author={Ruihang Chu and Yefei He and Zhekai Chen and Shiwei Zhang and Xiaogang Xu and Bin Xia and Dingdong Wang and Hongwei Yi and Xihui Liu and Hengshuang Zhao and Yu Liu and Yingya Zhang and Yujiu Yang},
      year={2025},
      eprint={2512.08765},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the license.

We would like to thank the contributors to the Wan, CoTracker, umt5-xxl, and HuggingFace repositories, for their open research.