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    This survey provides a comprehensive overview of Generative Adversarial Network (GAN)-based Facial Attribute Manipulation (FAM) methods. It details motivations, technical aspects, and future research directions for GAN-based FAM, aiding researchers in this computer vision domain.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Facial Attribute Manipulation (FAM) is crucial for applications like digital entertainment and biometrics.
    • Generative Adversarial Networks (GANs) have shown remarkable success in realistic image synthesis, driving FAM advancements.

    Purpose of the Study:

    • To present a comprehensive survey of GAN-based FAM methods.
    • To summarize the principal motivations and technical details of these methods.
    • To provide a reference for researchers entering or working in the FAM field.

    Main Methods:

    • Systematic review of GAN-based FAM techniques.
    • Categorization of methods into three main groups.
    • Analysis of FAM method properties, open issues, and future research.

    Main Results:

    • Detailed overview of GAN-based FAM approaches.
    • Identification of key trends and challenges in the field.
    • Structured categorization of existing literature.

    Conclusions:

    • GAN-based FAM is a rapidly evolving field with significant practical applications.
    • This survey offers a foundational understanding and points towards future research avenues.
    • The paper serves as a valuable resource for the computer vision community.