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arXiv · 2507.06689

Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis

Hao Tang·Ling Shao·Zhenyu Zhang·Luc Van Gool·Nicu Sebe
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Abstract

We propose a novel spatial-temporal graph Mamba (STG-Mamba) for the music-guided dance video synthesis task, i.e., to translate the input music to a dance video. STG-Mamba consists of two translation mappings: music-to-skeleton translation and skeleton-to-video translation. In the music-to-skeleton translation, we introduce a novel spatial-temporal graph Mamba (STGM) block to effectively construct skeleton sequences from the input music, capturing dependencies between joints in both the spatial and temporal dimensions. For the skeleton-to-video translation, we propose a novel self-supervised regularization network to translate the generated skeletons, along with a conditional image, into a dance video. Lastly, we collect a new skeleton-to-video translation dataset from the Internet, containing 54,944 video clips. Extensive experiments demonstrate that STG-Mamba achieves significantly better results than existing methods.

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Hao Tang, Ling Shao, Zhenyu Zhang, Luc Van Gool, Nicu Sebe. 2025-07-09. Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis. https://arxiv.org/abs/2507.06689

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