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

Hao Tang, Ling Shao, Zhenyu Zhang

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    Summary
    This summary is machine-generated.

    This study introduces STG-Mamba, a new AI model for generating dance videos from music. It effectively translates music into human skeleton movements and then into realistic dance videos, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Music-guided dance video synthesis is a challenging task requiring accurate translation of auditory input into complex human motion.
    • Existing methods often struggle to capture the intricate spatial and temporal dynamics inherent in dance movements.

    Purpose of the Study:

    • To propose a novel spatial-temporal graph Mamba (STG-Mamba) model for music-guided dance video synthesis.
    • To develop effective modules for both music-to-skeleton and skeleton-to-video translation.

    Main Methods:

    • Introduced a spatial-temporal graph Mamba (STGM) block to model music-to-skeleton translation, capturing joint dependencies across space and time.
    • Developed a self-supervised regularization network for skeleton-to-video translation, incorporating conditional images.
    • Created and utilized a new dataset of 54,944 video clips for skeleton-to-video translation.

    Main Results:

    • The STG-Mamba model demonstrated superior performance in music-guided dance video synthesis compared to existing approaches.
    • The STGM block effectively captured complex spatial-temporal dependencies in skeleton sequences.
    • The self-supervised regularization network enabled accurate translation of skeletons into realistic dance videos.

    Conclusions:

    • STG-Mamba offers a significant advancement in AI-driven dance video generation from music.
    • The proposed STGM block and regularization network are effective components for this task.
    • The developed dataset facilitates further research in skeleton-to-video translation.