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    This study introduces ReactFace, a new framework for generating diverse and appropriate listener facial reactions to speaker behavior. It models reaction distributions and synchronizes them with speech for realistic interactions.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Predicting listener facial reactions in dyadic interactions is complex due to diverse appropriate responses.
    • Previous methods focused on deterministic interpolation, failing to capture reaction diversity and contextual dependencies.
    • Existing approaches often lacked synchrony and appropriateness in generated facial reactions.

    Purpose of the Study:

    • To reformulate facial reaction prediction as an extrapolation/prediction problem.
    • To propose a novel framework, ReactFace, for generating multiple, diverse, and appropriate facial reactions.
    • To address limitations in modeling interaction context, synchrony, and reaction diversity.

    Main Methods:

    • Developed ReactFace framework to generate multiple appropriate facial reactions.
    • Learned facial reaction distributions to represent diverse responses.
    • Synchronized generated reactions with speaker verbal and non-verbal behaviors for realistic sequences.
    • Incorporated novel speaker-listener interaction modules.

    Main Results:

    • Demonstrated effectiveness in generating multiple diverse, synchronized, and appropriate facial reactions.
    • Achieved photo-realistic facial reactions tied to speaker's speech and expressions.
    • Validated the framework's ability to predict varied listener responses.

    Conclusions:

    • ReactFace successfully generates diverse and appropriate facial reactions, overcoming limitations of previous interpolation-based methods.
    • The framework enhances realism by synchronizing reactions with speaker behavior.
    • This approach advances the field of affective computing and human-computer interaction.