Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis
This addresses the music-guided dance video synthesis task for applications in entertainment or animation, but it appears incremental as it builds on prior translation-based approaches.
The paper tackles the problem of synthesizing dance videos from music by proposing a spatial-temporal graph Mamba (STG-Mamba) method, which achieves significantly better results than existing methods through two translation mappings and a new dataset of 54,944 video clips.
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.