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. In this paper, we propose DS-VTON, a dual-scale virtual try-on framework that explicitly disentangles these objectives for more effective modeling. 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. The second stage introduces a residual-guided diffusion process that reconstructs high-resolution outputs by refining the residual between the two scales, focusing on texture fidelity. In addition, our method adopts a fully mask-free generation paradigm, eliminating reliance on human parsing maps or segmentation masks. By leveraging the semantic priors embedded in pretrained diffusion models, this design more effectively preserves the person's appearance and geometric consistency. Extensive experiments demonstrate that DS-VTON achieves state-of-the-art performance in both structural alignment and texture preservation across multiple standard virtual try-on benchmarks.
Abstract (translated)
尽管最近取得了进展，大多数现有的虚拟试穿方法仍然难以同时解决两个核心挑战：准确地将服装图像与目标人体对齐以及保留精细的服装纹理和图案。在本文中，我们提出了DS-VTON，这是一种双尺度虚拟试穿框架，该框架明确分离了这些目标以进行更有效的建模。DS-VTON包含两阶段：第一阶段生成低分辨率的试穿结果，捕获服装与人体之间的语义对应关系，减少细节有助于结构对齐的稳健性；第二阶段引入了一种残差引导的扩散过程，通过细化两个尺度之间的残差来重建高分辨率输出，并专注于纹理保真度。此外，我们的方法采用了一个完全不依赖于掩码的生成范式，消除了对人类解析图或分割掩码的依赖。通过利用预训练扩散模型中嵌入的语义先验，这种设计更有效地保持了人物的外观和几何一致性。广泛的实验表明，DS-VTON在多个标准虚拟试穿基准测试中的结构对齐和纹理保真度方面均达到了最先进的性能。
URL
https://arxiv.org/abs/2506.00908
