D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On

• URL: http://arxiv.org/abs/2407.15111v1
• Date: Sun, 21 Jul 2024 10:40:53 GMT
• Title: D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On
• Authors: Zhaotong Yang, Zicheng Jiang, Xinzhe Li, Huiyu Zhou, Junyu Dong, Huaidong Zhang, Yong Du,
• Abstract summary: D$4$-VTON is an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before and after garment warping.
• Score: 32.73798955587999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce D$^4$-VTON, an innovative solution for image-based virtual try-on. We address challenges from previous studies, such as semantic inconsistencies before and after garment warping, and reliance on static, annotation-driven clothing parsers. Additionally, we tackle the complexities in diffusion-based VTON models when handling simultaneous tasks like inpainting and denoising. Our approach utilizes two key technologies: Firstly, Dynamic Semantics Disentangling Modules (DSDMs) extract abstract semantic information from garments to create distinct local flows, improving precise garment warping in a self-discovered manner. Secondly, by integrating a Differential Information Tracking Path (DITP), we establish a novel diffusion-based VTON paradigm. This path captures differential information between incomplete try-on inputs and their complete versions, enabling the network to handle multiple degradations independently, thereby minimizing learning ambiguities and achieving realistic results with minimal overhead. Extensive experiments demonstrate that D$^4$-VTON significantly outperforms existing methods in both quantitative metrics and qualitative evaluations, demonstrating its capability in generating realistic images and ensuring semantic consistency.

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