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

TL;DR

D$^4$-VTON introduces a novel diffusion-based virtual try-on framework that employs dynamic semantic disentangling and differential information tracking to improve garment warping accuracy and semantic consistency in image-based virtual try-on.

Contribution

The paper proposes D$^4$-VTON, a new paradigm combining dynamic semantics disentangling modules and differential information tracking for enhanced virtual try-on performance.

Findings

Outperforms existing methods in quantitative metrics.

Generates more realistic and semantically consistent try-on images.

Effectively handles multiple degradations with minimal overhead.

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.