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ViTon-GUN: Person-to-Person Virtual Try-on via Garment Unwrapping.

Nannan Zhang, Zhenyu Xie, Zhengwentai Sun

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    This study introduces ViTon-GUN for virtual try-on, improving garment deformation accuracy. The novel framework enhances Person-to-Person (P2P) try-on by unwrapping garments to a canonical pose.

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    Area of Science:

    • Computer Vision
    • Human-Centric Image Generation
    • Virtual Try-On Technology

    Background:

    • Person-to-Person (P2P) virtual try-on is a key application in human-centric image generation.
    • Existing methods face challenges in accurately modeling clothing deformation during direct garment transfer.

    Purpose of the Study:

    • To develop a novel framework, ViTon-GUN, for improved Person-to-Person (P2P) virtual try-on.
    • To address limitations in learning clothing deformation in existing P2P virtual try-on approaches.

    Main Methods:

    • Proposing ViTon-GUN, a framework dividing P2P into Person-to-Garment (P2G) and Garment-to-Person (G2P) subtasks.
    • Implementing a flow-based P2G scheme with an A-Pose estimator for garment unwrapping.
    • Utilizing a step-wise strategy and a 'plug-and-play' P2G module to adapt pre-trained G2P models.

    Main Results:

    • ViTon-GUN demonstrates superior performance in P2P virtual try-on tasks.
    • The framework effectively handles complex garment deformations, including intricate dress designs.
    • The P2G module allows adaptation of existing models without retraining.

    Conclusions:

    • ViTon-GUN significantly advances P2P virtual try-on capabilities.
    • The proposed method offers a flexible and efficient solution for virtual garment transfer.
    • This framework enhances the realism and accuracy of virtual try-on applications.