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Spatial-Aware Texture Transformer for High-Fidelity Garment Transfer.

Ting Liu, Jianfeng Zhang, Xuecheng Nie

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    This study introduces the SPatial-Aware Texture Transformer (SPATT) for realistic garment transfer. SPATT effectively handles pose and view differences, preserving texture details for improved virtual try-on applications.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Garment transfer aims to virtually dress a target person with clothing from a source model.
    • Existing methods struggle with variations in pose, body shape, and view, leading to unrealistic results.
    • Challenges include garment deformation, inferring unseen clothing areas, and preserving fine texture details.

    Purpose of the Study:

    • To propose a novel model, the SPatial-Aware Texture Transformer (SPATT), for realistic garment transfer.
    • To address the limitations of existing methods in handling diverse person attributes and maintaining visual fidelity.
    • To improve the accuracy and realism of virtual clothing applications.

    Main Methods:

    • Leveraging UV-space spatial prior information for correspondence and appearance inference.
    • Transforming source images into partial UV texture maps guided by dense pose estimation.
    • Proposing a coordinate-prior map and a spatial-aware texture generation network to complete UV textures.
    • Employing a refinement generative network for detail enhancement and realism.

    Main Results:

    • SPATT generates more realistic images compared to existing frameworks.
    • The model effectively preserves fine texture details during garment transfer.
    • SPATT successfully handles challenging cases with significant pose and view differences between individuals.

    Conclusions:

    • The proposed SPATT model offers a significant advancement in garment transfer technology.
    • The use of UV-space priors and spatial-aware networks enhances the realism and detail preservation.
    • SPATT demonstrates robust performance across various challenging scenarios, paving the way for improved virtual try-on and fashion applications.