DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On

TL;DR

DS-VTON introduces a dual-scale coarse-to-fine framework for virtual try-on that improves garment alignment and texture preservation without relying on segmentation masks, achieving state-of-the-art results.

Contribution

The paper presents a novel dual-stage approach combining low-res structural alignment with high-res texture refinement using diffusion processes, eliminating the need for human parsing maps.

Findings

Outperforms previous methods in alignment accuracy

Enhances texture detail preservation in try-on results

Operates without segmentation masks

Abstract

Despite recent progress, most existing virtual try-on methods still struggle to simultaneously address two core challenges: accurately aligning the garment image with the target human body, and preserving fine-grained garment textures and patterns. These two requirements map directly onto a coarse-to-fine generation paradigm, where the coarse stage handles structural alignment and the fine stage recovers rich garment details. Motivated by this observation, we propose DS-VTON, an enhanced dual-scale coarse-to-fine framework that tackles the try-on problem more effectively. DS-VTON consists of two stages: the first stage generates a low-resolution try-on result to capture the semantic correspondence between garment and body, where reduced detail facilitates robust structural alignment. In the second stage, a blend-refine diffusion process reconstructs high-resolution outputs by refining the residual between scales through noise-image blending, emphasizing texture fidelity and effectively correcting fine-detail errors from the low-resolution stage. In addition, our method adopts a fully mask-free generation strategy, eliminating reliance on human parsing maps or segmentation masks. Extensive experiments show that DS-VTON not only achieves state-of-the-art performance but consistently and significantly surpasses prior methods in both structural alignment and texture fidelity across multiple standard virtual try-on benchmarks.