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Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony.

Chao Xu, Mingze Sun, Zhi-Qi Cheng

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    Summary

    This study introduces Combo, a framework for generating 3D human motions from speech. It efficiently adapts motion generation for customizable identity and emotion, overcoming multiple-input-multiple-output challenges.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Generating realistic 3D human motion synchronized with speech is complex.
    • Existing models struggle with multiple inputs (speech, identity, emotion) and coordinating facial/body movements.

    Purpose of the Study:

    • To propose a novel framework, Combo, for harmonious co-speech 3D human motion generation.
    • To enable efficient and customizable adaptation of generated motions for identity and emotion.

    Main Methods:

    • Introduced a pre-training strategy on neutral data, followed by fine-tuning with a novel X-Adapter for efficient adaptation.
    • Developed DU-Trans, a transformer architecture that branches for facial and body motion, then merges for coordinated prediction.

    Main Results:

    • Combo effectively generates high-quality, holistic 3D human motions synchronized with speech.
    • The framework demonstrates efficiency in adapting motions for varying identity and emotion.

    Conclusions:

    • Combo addresses the MIMO challenge in motion generation.
    • The proposed methods offer a robust and adaptable solution for co-speech motion synthesis.