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    This survey overviews deep learning for face generation and editing using StyleGAN. It covers StyleGAN evolution, latent spaces, image editing, stylization, restoration, and Deepfake applications.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Generative Adversarial Networks (GANs) have revolutionized image synthesis.
    • StyleGAN, a prominent GAN architecture, excels in high-resolution face generation.
    • Advancements in deep learning necessitate a consolidated overview of StyleGAN's capabilities.

    Purpose of the Study:

    • To provide a comprehensive survey of deep learning methods for face generation and editing.
    • To detail the evolution of StyleGAN architectures, from PGGAN to StyleGAN3.
    • To explore key aspects including training metrics, latent representations, GAN inversion, and diverse applications.

    Main Methods:

    • Literature review of state-of-the-art deep learning techniques for face manipulation.
    • Analysis of StyleGAN's architectural progression and its impact on generative quality.
    • Categorization of applications: face editing, stylization, restoration, and Deepfakes.

    Main Results:

    • StyleGAN has evolved significantly, enabling increasingly realistic and controllable face synthesis.
    • Various techniques for GAN inversion and latent space manipulation allow for precise editing.
    • Applications range from artistic stylization to practical face restoration and the generation of Deepfakes.

    Conclusions:

    • StyleGAN represents a powerful framework for advanced face generation and editing tasks.
    • The field continues to evolve, with ongoing research in latent space control and application diversity.
    • This survey serves as an accessible entry point for understanding StyleGAN-based face manipulation.