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

• URL: http://arxiv.org/abs/2506.00908v2
• Date: Sun, 05 Oct 2025 06:39:48 GMT
• Title: DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On
• Authors: Xianbing Sun, Yan Hong, Jiahui Zhan, Jun Lan, Huijia Zhu, Weiqiang Wang, Liqing Zhang, Jianfu Zhang,
• Abstract summary: We propose DS-VTON, an enhanced dual-scale coarse-to-fine framework for virtual try-on.<n> DS-VTON consists of two stages: the first generates a low-resolution try-on result to capture the semantic correspondence between garment and body.<n>In the second stage, a blend-refine diffusion process reconstructs high-resolution outputs by refining the residual between scales.
• Score: 33.05238077456732
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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