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About this paper
Zikai Huang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Chenxi Zheng, Jing Qin, Shengfeng He
Beat-It, a diffusion-based framework, generates 3D dance with precise beat synchronization and key pose guidance by explicitly disentangling beat conditions from music and employing a hierarchical multi-condition fusion. This method achieved a Beat Alignment Score of 0.661 and a Key Pose Distance of 0.306 on the AIST++ dataset, surpassing previous state-of-the-art methods like EDGE, and demonstrated robustness in real-world applications.
Key insights
- Representing beat conditions as the distance to the nearest beat frame offers richer temporal context than binary signals, enhancing rhythmic understanding for the generative model.
- A hierarchical fusion strategy, particularly with dynamic beat-aware mask dilation, effectively resolves conflicts and leverages complementary information from sparse keyframes and dense music/beat inputs.
- Explicit supervision through a custom beat alignment loss, coupled with adaptive weighting, directly improves the model's ability to achieve high-fidelity beat synchronization in generated motions.
Summary by alphaXiv.
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