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Pattern Guided UV Recovery for Realistic Video Garment Texturing.

Youyi Zhan, Tuanfeng Y Wang, Tianjia Shao

    IEEE Transactions on Visualization and Computer Graphics
    |January 16, 2024
    PubMed
    Summary

    This study introduces an automated method for replacing garment textures in videos, reducing labor for virtual fashion showcases. The pattern-based approach enhances online fashion e-commerce by enabling efficient texture customization.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • E-commerce Technology

    Background:

    • The rapid growth of e-commerce in fashion necessitates efficient virtual showcasing of garments with diverse textures.
    • Current methods for virtual texture replacement in videos are labor-intensive, relying on manual editing or real-time capture.

    Purpose of the Study:

    • To develop an automated, pattern-based approach for UV and shading recovery from real videos.
    • To enable automatic garment texture replacement for virtual fashion showcases.

    Main Methods:

    • A per-pixel UV regression module using blended-weight multilayer perceptrons (MLPs) driven by detected cloth pattern correspondences.
    • A novel loss function on the Jacobian of UV mapping to ensure seamless texture application, especially around folds and occlusions.
    • Temporal constraints for consistent UV prediction across video frames.

    Main Results:

    • Robust performance across various clothing types, lighting conditions, and motion challenges.
    • Plausible texture replacement results with preserved garment folding and overlapping.
    • Significant qualitative and quantitative improvements over existing baseline methods.

    Conclusions:

    • The proposed pattern-based approach automates texture replacement in fashion videos, significantly reducing manual effort.
    • This method offers a one-click solution with potential to boost the growth of fashion e-commerce.
    • The technique preserves intricate garment details like folds and overlaps, enhancing virtual product representation.