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    Summary

    ExGes, a novel framework, enhances audio-driven human gesture synthesis by improving expressiveness and semantic relevance. This retrieval-enhanced diffusion model generates more natural and diverse human motions for virtual avatars and HCI applications.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Audio-driven human gesture synthesis is vital for virtual avatars and HCI.
    • Existing methods struggle with coarse gestures, limited expressiveness, and poor audio-semantic alignment.

    Purpose of the Study:

    • To introduce ExGes, a novel retrieval-enhanced diffusion framework for improved audio-driven human gesture synthesis.
    • To enhance gesture naturalness, expressiveness, and semantic relevance to audio inputs.

    Main Methods:

    • Developed a Motion Base Construction to create a gesture library.
    • Implemented a Motion Retrieval Module using contrastive learning and momentum distillation for fine-grained pose retrieval.
    • Integrated a Precise Control Module with partial and stochastic masking for flexible control.

    Main Results:

    • ExGes reduced Fréchet Gesture Distance by 4.55% and improved motion diversity by 5.3% compared to EMAGE on the BEAT2 dataset.
    • User studies showed a 71.3% preference for ExGes in terms of naturalness and semantic relevance.

    Conclusions:

    • ExGes offers a significant advancement in audio-driven human gesture synthesis.
    • The framework provides more natural, expressive, and semantically relevant gestures compared to existing methods.