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    We introduce a new dataset and method for creating expressive talking avatars, enabling realistic emotional communication in virtual reality (VR). This advances the animation of stylized characters for enhanced virtual interactions.

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    Area of Science:

    • Computer Graphics and Virtual Reality
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Stylized avatars are crucial for virtual reality (VR) collaboration, but generating explicit emotional expressions remains challenging.
    • Existing methods often fail due to reliance on human facial features, which are unsuitable for stylized avatar designs.

    Purpose of the Study:

    • To address the difficulty of generating emotional expressions in stylized avatars.
    • To introduce a novel dataset and a corresponding generation method for emotional talking avatars.

    Main Methods:

    • Developed the Emotional Talking Avatar Dataset, a video corpus of 6 stylized characters displaying 7 distinct emotions.
    • Created an accompanying emotional talking avatar generation method allowing for emotion manipulation.
    • Validated the approach through audio-based puppetry, state-of-the-art comparisons, and a user study.

    Main Results:

    • Demonstrated the effectiveness of the dataset and generation method in producing emotionally expressive talking avatars.
    • User study confirmed the method's capability in generating convincing emotional expressions for stylized characters.
    • The proposed method outperforms existing techniques in generating audio-driven avatar animations.

    Conclusions:

    • The Emotional Talking Avatar Dataset and generation method significantly advance the creation of expressive stylized avatars.
    • This work facilitates more natural and engaging communication in virtual reality environments.
    • The developed technique has broad applications in animating avatars for immersive virtual experiences.