{"ID":2867646,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17654","arxiv_id":"2509.17654","title":"Clothing agnostic Pre-inpainting Virtual Try-ON","abstract":"With the development of deep learning technology, virtual try-on technology has devel-oped important application value in the fields of e-commerce, fashion, and entertainment. The recently proposed Leffa technology has addressed the texture distortion problem of diffusion-based models, but there are limitations in that the bottom detection inaccuracy and the existing clothing silhouette persist in the synthesis results. To solve this problem, this study proposes CaP-VTON (Clothing Agnostic Pre-Inpainting Virtual Try-On). CaP-VTON integrates DressCode-based multi-category masking and Stable Diffu-sion-based skin inflation preprocessing; in particular, a generated skin module was in-troduced to solve skin restoration problems that occur when long-sleeved images are con-verted to short-sleeved or sleeveless ones, introducing a preprocessing structure that im-proves the naturalness and consistency of full-body clothing synthesis, and allowing the implementation of high-quality restoration considering human posture and color. As a result, CaP-VTON achieved 92.5%, which is 15.4% better than Leffa, in short-sleeved syn-thesis accuracy, and consistently reproduced the style and shape of the reference clothing in visual evaluation. These structures maintain model-agnostic properties and are appli-cable to various diffusion-based virtual inspection systems; they can also contribute to applications that require high-precision virtual wearing, such as e-commerce, custom styling, and avatar creation.","short_abstract":"With the development of deep learning technology, virtual try-on technology has devel-oped important application value in the fields of e-commerce, fashion, and entertainment. The recently proposed Leffa technology has addressed the texture distortion problem of diffusion-based models, but there are limitations in that...","url_abs":"https://arxiv.org/abs/2509.17654","url_pdf":"https://arxiv.org/pdf/2509.17654v3","authors":"[\"Sehyun Kim\",\"Hye Jun Lee\",\"Jiwoo Lee\",\"Taemin Lee\"]","published":"2025-09-22T11:58:20Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}