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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Generative Models

    Background:

    • Deep learning has advanced sketch-to-portrait generation.
    • StyleGAN offers state-of-the-art generation but lacks sketch-friendliness due to unconditional nature.
    • Preserving spatial information is crucial for sketch-based image synthesis.

    Purpose of the Study:

    • To develop a StyleGAN-based framework that directly incorporates spatial information for sketch-to-portrait generation.
    • To enhance the control and precision of generating realistic face images from user inputs like sketches and semantic maps.
    • To create an intuitive drawing interface for non-professional users to produce high-quality portraits.

    Main Methods:

    • Propose Spatially Conditioned StyleGAN (SC-StyleGAN) by injecting spatial constraints into the StyleGAN framework.
    • Utilize sketches and semantic maps as input modalities for precise user guidance.
    • Develop the DrawingInStyles interface for user-friendly portrait creation and editing.

    Main Results:

    • SC-StyleGAN demonstrates superior generation capabilities compared to existing methods.
    • The system effectively preserves spatial information from input sketches.
    • Qualitative and quantitative evaluations confirm the method's effectiveness.

    Conclusions:

    • SC-StyleGAN offers a robust solution for controlled sketch-to-portrait generation.
    • The DrawingInStyles system provides an accessible tool for creating realistic face images.
    • The approach enhances both the quality of generated portraits and user interaction experience.