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    Summary
    This summary is machine-generated.

    This study introduces a bionic face sketch generator that synthesizes detailed facial sketches. The novel approach enhances digital entertainment and aids art students by producing coarse, fine, and finer sketch outputs.

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    Area of Science:

    • Computer Vision and Graphics
    • Artificial Intelligence
    • Digital Entertainment

    Background:

    • Current face sketch synthesis methods often produce coarse structures lacking fine facial details.
    • Digital entertainment applications require high-fidelity face sketch generation.

    Purpose of the Study:

    • To develop an advanced face sketch generator capable of synthesizing delicate and striking facial details.
    • To provide a tool that assists art students in learning sketch techniques with varying levels of detail.

    Main Methods:

    • A novel bionic face sketch generator with three hierarchical parts: coarse, fine, and finer.
    • Utilizes a generative adversarial network (GAN) with U-Net for facial structure generation.
    • Employs a probabilistic graphical model for refining details and a convolutional neural network (CNN) for high-frequency details.

    Main Results:

    • The proposed generator successfully synthesizes face sketches with significantly more delicate and striking details.
    • Achieves superior visual effects and quantitative metrics compared to existing state-of-the-art methods.
    • Generates multiple sketch versions (coarse, fine, finer) beneficial for educational purposes.

    Conclusions:

    • The bionic face sketch generator effectively addresses the limitations of existing methods by generating high-quality, detailed sketches.
    • The approach meets user demands in digital entertainment and offers valuable learning resources for art students.
    • Demonstrates a significant advancement in the field of automatic face sketch synthesis.