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    This study introduces a novel deep-learning framework for automatic 3D model recoloring. It efficiently transfers colors from reference images to 3D textures, simplifying a complex process.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Recoloring 3D models is complex, demanding expertise and manual work.
    • Existing methods lack efficiency and automation for texture transfer.

    Purpose of the Study:

    • To present the first deep-learning framework for exemplar-based 3D model recoloring.
    • To automate the color transfer process from reference images to 3D model textures.

    Main Methods:

    • A novel feed-forward Color Transfer Network for semantic-level color transfer using dense correspondences.
    • A 3D Texture Optimization Module to handle 3D model constraints like UV mapping for seamless textures.

    Main Results:

    • The framework successfully transfers colors from reference images to 3D model textures.
    • The proposed modules address key challenges in 3D color transfer, ensuring high-quality results.

    Conclusions:

    • The developed deep-learning framework offers a robust and generalized solution for 3D model recoloring.
    • This method significantly reduces the manual effort and expertise required for 3D texture manipulation.