Shape-Guided Clothing Warping for Virtual Try-On

Fuente: arXiv
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Autores principales: Han, Xiaoyu, Zheng, Shunyuan, Li, Zonglin, Wang, Chenyang, Sun, Xin, Meng, Quanling
Formato: Preprint
Publicado: 2025
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author Han, Xiaoyu
Zheng, Shunyuan
Li, Zonglin
Wang, Chenyang
Sun, Xin
Meng, Quanling
author_facet Han, Xiaoyu
Zheng, Shunyuan
Li, Zonglin
Wang, Chenyang
Sun, Xin
Meng, Quanling
contents Image-based virtual try-on aims to seamlessly fit in-shop clothing to a person image while maintaining pose consistency. Existing methods commonly employ the thin plate spline (TPS) transformation or appearance flow to deform in-shop clothing for aligning with the person's body. Despite their promising performance, these methods often lack precise control over fine details, leading to inconsistencies in shape between clothing and the person's body as well as distortions in exposed limb regions. To tackle these challenges, we propose a novel shape-guided clothing warping method for virtual try-on, dubbed SCW-VTON, which incorporates global shape constraints and additional limb textures to enhance the realism and consistency of the warped clothing and try-on results. To integrate global shape constraints for clothing warping, we devise a dual-path clothing warping module comprising a shape path and a flow path. The former path captures the clothing shape aligned with the person's body, while the latter path leverages the mapping between the pre- and post-deformation of the clothing shape to guide the estimation of appearance flow. Furthermore, to alleviate distortions in limb regions of try-on results, we integrate detailed limb guidance by developing a limb reconstruction network based on masked image modeling. Through the utilization of SCW-VTON, we are able to generate try-on results with enhanced clothing shape consistency and precise control over details. Extensive experiments demonstrate the superiority of our approach over state-of-the-art methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/SCW-VTON.
format Preprint
id arxiv_https___arxiv_org_abs_2504_15232
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Shape-Guided Clothing Warping for Virtual Try-On
Han, Xiaoyu
Zheng, Shunyuan
Li, Zonglin
Wang, Chenyang
Sun, Xin
Meng, Quanling
Computer Vision and Pattern Recognition
Image-based virtual try-on aims to seamlessly fit in-shop clothing to a person image while maintaining pose consistency. Existing methods commonly employ the thin plate spline (TPS) transformation or appearance flow to deform in-shop clothing for aligning with the person's body. Despite their promising performance, these methods often lack precise control over fine details, leading to inconsistencies in shape between clothing and the person's body as well as distortions in exposed limb regions. To tackle these challenges, we propose a novel shape-guided clothing warping method for virtual try-on, dubbed SCW-VTON, which incorporates global shape constraints and additional limb textures to enhance the realism and consistency of the warped clothing and try-on results. To integrate global shape constraints for clothing warping, we devise a dual-path clothing warping module comprising a shape path and a flow path. The former path captures the clothing shape aligned with the person's body, while the latter path leverages the mapping between the pre- and post-deformation of the clothing shape to guide the estimation of appearance flow. Furthermore, to alleviate distortions in limb regions of try-on results, we integrate detailed limb guidance by developing a limb reconstruction network based on masked image modeling. Through the utilization of SCW-VTON, we are able to generate try-on results with enhanced clothing shape consistency and precise control over details. Extensive experiments demonstrate the superiority of our approach over state-of-the-art methods both qualitatively and quantitatively. The code is available at https://github.com/xyhanHIT/SCW-VTON.
title Shape-Guided Clothing Warping for Virtual Try-On
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2504.15232