  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.
