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Learning Disentangled Representation for One-Shot Progressive Face Swapping.

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    FaceSwapper offers efficient one-shot face swapping using Generative Adversarial Networks. It improves identity representation and uses semantic information for accurate pose and expression, achieving state-of-the-art results with less data.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Face swapping technology is advancing but faces challenges with identity representation and semantic understanding.
    • Current methods often require extensive data and struggle with fixed identity features, limiting swap quality.

    Purpose of the Study:

    • To introduce FaceSwapper, an efficient one-shot face swapping method.
    • To enhance face swapping by disentangling identity and attribute information and incorporating semantic guidance.

    Main Methods:

    • Developed a novel method, FaceSwapper, utilizing Generative Adversarial Networks (GANs).
    • Implemented a disentangled representation module with flexible identity and detailed attribute encoders.
    • Introduced a semantic-guided fusion module for precise control over swapped regions, pose, and expressions.

    Main Results:

    • Achieved state-of-the-art performance on benchmark face swapping datasets.
    • Demonstrated effectiveness with significantly fewer training samples compared to existing methods.
    • Showcased progressive face swapping capabilities due to disentangled representations.

    Conclusions:

    • FaceSwapper provides a flexible and efficient solution for one-shot face swapping.
    • The integration of disentangled representations and semantic guidance leads to superior results.
    • The method offers a promising direction for improving identity preservation and attribute accuracy in face manipulation.