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MoveBench of Wan-Move

Paper Code Model Model Model Video Website

MoveBench: A Comprehensive and Well-Curated Benchmark to Access Motion Control in Videos

MoveBench evaluates fine-grained point-level motion control in generated videos. We categorize the video library from Pexels into 54 content categories, 10-25 videos each, giving rise to 1018 cases to ensure a broad scenario coverage. All video clips maintain a 5-second duration to facilitate evaluation of long-range dynamics. Every clip is paired with detailed motion annotations for a single object. Addtional 192 clips have motion annotations for multiple objects. We ensure annotation quality by developing an interactive labeling pipeline, marrying annotation precision with automated scalability.

Welcome everyone to use it!

Statistics

logo The contruction pipeline of MoveBench

logo Balanced sample number per video category

logo Comparison with related benchmarks

Download

Download MoveBench from Hugging Face:

huggingface-cli download Ruihang/MoveBench --local-dir ./MoveBench --repo-type dataset

Extract the files below:

tar -xzvf en.tar.gz
tar -xzvf zh.tar.gz

The file structure will be:

MoveBench
β”œβ”€β”€ en             # English version
β”‚   β”œβ”€β”€ single_track.txt
β”‚   β”œβ”€β”€ multi_track.txt
β”‚   β”œβ”€β”€ first_frame
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0.jpg
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_1.jpg
β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”œβ”€β”€ video
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0.mp4
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_1.mp4
β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”œβ”€β”€ track
β”‚   β”‚   β”œβ”€β”€ single
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_tracks.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_visibility.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”œβ”€β”€ multi
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_tracks.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_visibility.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”œβ”€β”€ zh             # Chinese version
β”‚   β”œβ”€β”€ single_track.txt
β”‚   β”œβ”€β”€ multi_track.txt
β”‚   β”œβ”€β”€ first_frame
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0.jpg
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_1.jpg
β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”œβ”€β”€ video
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0.mp4
β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_1.mp4
β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”œβ”€β”€ track
β”‚   β”‚   β”œβ”€β”€ single
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_tracks.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_visibility.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”œβ”€β”€ multi
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_tracks.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ Pexels_videoid_0_visibility.npy
β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”œβ”€β”€ bench.py   # Evaluation script
β”œβ”€β”€ utils      # Evaluation code modules

For evaluation, please refer to Wan-Move code base. Enjoy it!

Citation

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}
}

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