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StyleVR: Stylizing Character Animations With Normalizing Flows.

Bin Ji, Ye Pan, Yichao Yan

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    Summary
    This summary is machine-generated.

    This study introduces a new generative model for stylizing long, complex character animations in virtual reality (VR). The StyleVR system enables varied and controllable motion style transfer for animated virtual characters.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Virtual Reality

    Background:

    • Motion style is key for realistic animated virtual characters.
    • Existing style transfer models struggle with long, aperiodic animations and offer limited output variety.

    Purpose of the Study:

    • To develop a generative model for stylizing long and aperiodic animations in VR.
    • To enable varied and controllable motion style transfer for virtual characters.

    Main Methods:

    • Proposed a generative model based on normalizing flows.
    • Formulated motion style transfer and stylized motion generation as conditional normalizing flows with multi-class latent space.
    • Encoded high-frequency style features and used style-content labels for disentangled control.

    Main Results:

    • Developed a prototype system, StyleVR, integrated into Unity for VR applications.
    • Demonstrated superior performance in style transfer compared to existing methods.
    • Achieved stochastic stylized motion generation with greater variability.

    Conclusions:

    • The proposed normalizing flow model effectively addresses limitations of previous methods for long animation stylization.
    • StyleVR provides casual users with intuitive control over motion style and content in VR.
    • The approach enables diverse and controllable stylized motion generation for virtual characters.