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A Generative Audio-Visual Prosodic Model for Virtual Actors.

Adela Barbulescu, Remi Ronfard, Gerard Bailly

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    This study introduces a novel method for generating expressive virtual character animations. It converts neutral speech and animation into natural, emotionally resonant performances by learning prosodic patterns.

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    Area of Science:

    • Computer Animation
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Expressing complex mental states in virtual characters is challenging.
    • Coordinated prosody, facial expressions, and motion are crucial for realistic animation.
    • Existing methods often lack naturalness in conveying emotions.

    Purpose of the Study:

    • To develop an expressive conversion method for virtual character animation.
    • To generate natural speech and facial animation from neutral inputs.
    • To create recognizable emotional attitudes in digital characters.

    Main Methods:

    • Learning prototypical prosodic contours at the sentence level.
    • Utilizing a dataset of dramatic attitudes for training.
    • Inputting neutral speech and animation for conversion.

    Main Results:

    • Successful generation of natural speech and facial animation.
    • Creation of recognizable emotional attitudes.
    • Demonstration of an effective expressive conversion technique.

    Conclusions:

    • The proposed method enables the generation of expressive virtual character performances.
    • Learning prosodic contours is key to conveying complex mental states.
    • This approach advances realistic emotional expression in computer animation.