Clothing agnostic Pre-inpainting Virtual Try-ON

• URL: http://arxiv.org/abs/2509.17654v2
• Date: Tue, 23 Sep 2025 06:05:25 GMT
• Title: Clothing agnostic Pre-inpainting Virtual Try-ON
• Authors: Sehyun Kim, Hye Jun Lee, Jiwoo Lee, Taemin Lee,
• Abstract summary: CaP-VTON has improved the naturalness and consistency of whole-body clothing syn-thesis.<n> CaP-VTON recorded 92.5%, which is 15.4% better than Leffa in short-sleeved synthesis accuracy.
• Score: 1.7764619921640012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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