DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On
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arXiv
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| Format: | Preprint |
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2025
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| author | Sun, Xianbing Hong, Yan Zhan, Jiahui Lan, Jun Zhu, Huijia Wang, Weiqiang Zhang, Liqing Zhang, Jianfu |
| author_facet | Sun, Xianbing Hong, Yan Zhan, Jiahui Lan, Jun Zhu, Huijia Wang, Weiqiang Zhang, Liqing Zhang, Jianfu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00908 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On Sun, Xianbing Hong, Yan Zhan, Jiahui Lan, Jun Zhu, Huijia Wang, Weiqiang Zhang, Liqing Zhang, Jianfu Computer Vision and Pattern Recognition 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. |
| title | DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00908 |