D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On
<|reference_start|>D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion based Virtual Try-On: 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.<|reference_end|>
@article{yang2024d$^4$-vton:,
    title={D$^4$-VTON: Dynamic Semantics Disentangling for Differential Diffusion
  based Virtual Try-On},
    author={Zhaotong Yang, Zicheng Jiang, Xinzhe Li, Huiyu Zhou, Junyu Dong,
  Huaidong Zhang, Yong Du},
    journal={arXiv preprint arXiv:2407.15111},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15111},
    primaryClass={cs.CV}
}