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DynAvatar: Dynamic 3D Head Avatar Deformation With Expression Guided Gaussian Splatting.

Wenfeng Song, Zhongyong Ye, Zhenyu Wu

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    DynAvatar creates realistic 3D head avatars by integrating expression-guided deformation into 3D Gaussian splatting. This novel framework enhances facial animation for immersive applications.

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    Area of Science:

    • Computer Graphics
    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Generating high-fidelity 3D head avatars is crucial for immersive technologies like virtual reality and gaming.
    • Existing methods struggle with precise non-rigid facial deformation and semantically controlled expression synthesis.

    Purpose of the Study:

    • To introduce DynAvatar, a novel framework for creating photorealistic and emotionally resonant 3D head avatars.
    • To enable fine-grained, anatomically meaningful facial animation with semantic control.

    Main Methods:

    • DynAvatar integrates an expression-guided Gaussian deformation module with the 3D Gaussian splatting pipeline.
    • A spatial context embedding mechanism preserves semantic coherence and spatial consistency during expression generation.

    Main Results:

    • DynAvatar significantly improves visual realism and expression fidelity compared to state-of-the-art methods.
    • The framework demonstrates superior rendering quality across diverse datasets.

    Conclusions:

    • DynAvatar offers a robust solution for generating high-quality, expressive 3D head avatars.
    • The method advances the state-of-the-art in 3D avatar creation for immersive applications.