Abstract
With the development of deep learning technology, virtual try-on technology has become an important application value in the fields of e-commerce, fashion, and entertainment. The recently proposed Leffa has improved the texture distortion problem of diffu-sion-based models, but there are limitations in that the bottom detection inaccuracy and the existing clothing silhouette remain in the synthesis results. To solve this problem, this study proposes CaP-VTON (Clothing agnostic Pre-inpainting Virtual Try-ON). CaP-VTON has improved the naturalness and consistency of whole-body clothing syn-thesis by integrating multi-category masking based on Dress Code and skin inpainting based on Stable Diffusion. In particular, a generate skin module was introduced to solve the skin restoration problem that occurs when long-sleeved images are converted into short-sleeved or sleeveless ones, and high-quality restoration was implemented consider-ing the human body posture and color. As a result, CaP-VTON recorded 92.5\%, which is 15.4\% better than Leffa in short-sleeved synthesis accuracy, and showed the performance of consistently reproducing the style and shape of reference clothing in visual evaluation. These structures maintain model-agnostic properties and are applicable to various diffu-sion-based virtual inspection systems, and can contribute to applications that require high-precision virtual wearing, such as e-commerce, custom styling, and avatar creation.
Abstract (translated)
随着深度学习技术的发展，虚拟试穿技术在电子商务、时尚和娱乐领域变得越来越重要。最近提出的Leffa模型虽然改善了基于扩散的模型中的纹理失真问题，但仍存在底边检测不准确以及现有服装轮廓残留的问题。为解决这些问题，本研究提出了一种名为CaP-VTON（Clothing agnostic Pre-inpainting Virtual Try-ON）的新方法。CaP-VTON通过整合基于Dress Code的多类别遮罩和基于Stable Diffusion的人体皮肤修复技术，提高了全身服装合成的自然度与一致性。 特别是，该方法引入了一种生成皮肤模块来解决将长袖图像转换为短袖或无袖时出现的皮肤恢复问题，并考虑到人体姿态和颜色实现了高质量的恢复。实验结果显示，CaP-VTON在短袖合成准确率上达到了92.5%，比Leffa高出15.4%；视觉评估显示其能够一致地再现参考服装的风格和形状。这些结构保持了模型无关性属性，适用于各种基于扩散的虚拟试穿系统，并能为需要高精度虚拟穿衣的应用（如电子商务、定制造型以及头像创建）做出贡献。
URL
https://arxiv.org/abs/2509.17654
