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

Authors

Hao Tang, Ling Shao, Zhenyu Zhang, Luc Van Gool, Nicu Sebe

Published in

IEEE transactions on pattern analysis and machine intelligence. Volume PP. Jul 15, 2025. Epub Jul 15, 2025.

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.

PMID:
40663669
Bibliographic data and abstract were imported from PubMed on 16 Jul 2025.

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