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    Generative adversarial networks (GANs) learn disentangled face representations in their latent space. This framework, InterFaceGAN, enables attribute manipulation and artifact correction without retraining GAN models.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Generative adversarial networks (GANs) excel at photo-realistic image synthesis, particularly for faces.
    • Understanding the latent space representation learned by GANs remains a challenge.

    Purpose of the Study:

    • To interpret the disentangled face representation learned by state-of-the-art GAN models.
    • To study facial semantics encoded within the GAN latent space.
    • To enable attribute manipulation and artifact correction in synthesized faces.

    Main Methods:

    • Proposing InterFaceGAN framework for latent space interpretation.
    • Identifying linear subspaces corresponding to facial semantics.
    • Utilizing subspace projection for disentangling attributes.
    • Performing quantitative analysis of editing results.

    Main Results:

    • GANs learn facial semantics in linear subspaces of the latent space.
    • Identified subspaces allow realistic manipulation of attributes like age, gender, expression, and pose.
    • Improved disentanglement of semantics leads to precise attribute control.
    • Approach successfully applied to real face editing via GAN inversion.

    Conclusions:

    • Synthesizing faces with GANs inherently learns a disentangled and controllable face representation.
    • InterFaceGAN provides a method for interpreting and manipulating this learned representation.
    • The framework facilitates attribute editing and artifact correction in GAN-generated faces.