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    This study introduces attribute factorization (AF) for face reenactment, disentangling features without paired images. This method enables controllable generation of talking faces by separating relevant and irrelevant attributes.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Face reenactment synthesizes talking faces from source images.
    • Learning latent disentanglement is key for domain mapping between source and target faces.
    • Existing methods often require paired data for attribute manipulation.

    Purpose of the Study:

    • To present an information-theoretic attribute factorization (AF) for flow-based face reenactment.
    • To disentangle mixed features into attribute-relevant and attribute-irrelevant components.
    • To enable controllable generation of talking faces without paired images.

    Main Methods:

    • Utilized flow-based models for latent variable factorization.
    • Developed attribute factorization (AF) to separate essential facial features.
    • Employed multiple losses: source/target structure, random-pair reconstruction, and sequential classification.
    • Introduced a mutual information flow for improved disentanglement and domain mapping.

    Main Results:

    • Successfully disentangled latent variables into attribute-relevant and irrelevant components.
    • Demonstrated effective face reenactment, specifically mouth reenactment, using the proposed AF.
    • Showcased the ability to generate conditional talking face sequences with meaningful interpretations.
    • Validated the method's performance in conditional generation and mapping tasks.

    Conclusions:

    • Attribute factorization (AF) provides an effective approach for disentangling features in face reenactment.
    • The proposed method achieves controllable generation of talking faces without requiring paired training data.
    • Experiments confirm the utility of AF for conditional generation and domain mapping in face reenactment.