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SpeechAct: Towards Generating Whole-Body Motion From Speech.

Jinsong Zhang, Minjie Zhu, Yuxiang Zhang

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    SpeechAct generates realistic and diverse whole-body motions from speech using a novel hybrid point representation and contrastive learning. This method enhances computer graphics and virtual reality applications by improving motion naturalness and distinctiveness.

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    Area of Science:

    • Computer Graphics
    • Virtual Reality
    • Human-Computer Interaction

    Background:

    • Generating natural and diverse whole-body motions from speech is essential for immersive experiences.
    • Existing methods often lack realism and diversity in speech-driven motion generation.

    Purpose of the Study:

    • To introduce SpeechAct, a novel method for generating realistic and diverse whole-body motions from speech audio.
    • To improve the naturalness, smoothness, and distinctiveness of generated motions.

    Main Methods:

    • Utilizing a hybrid point representation combining keypoint and surface points of 3D body models.
    • Employing a VQ-VAE for motion codebook learning and a translation model for audio-to-motion regression.
    • Implementing contrastive motion learning with negative samples to enhance motion diversity.

    Main Results:

    • The hybrid point representation facilitates smooth and natural motion generation.
    • Contrastive learning effectively increases the diversity of generated motions.
    • An integrated face generator ensures deterministic and synchronized facial movements.

    Conclusions:

    • SpeechAct demonstrates superior performance in generating realistic and diverse speech-driven whole-body motions.
    • The proposed hybrid representation and contrastive learning are effective for enhancing motion quality.
    • The method has significant potential for applications in computer graphics and immersive VR/AR.