{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enhancing-person-to-person-virtual-try-on","title":"Enhancing Person-to-Person Virtual Try-On with Multi-Garment Virtual Try-Off","arxiv_id":"2504.13078","date":"2025-04-17","proceeding":null,"authors":["Riza Velioglu","Petra Bevandic","Robin Chan","Barbara Hammer"],"abstract":"Computer vision is transforming fashion through Virtual Try-On (VTON) and Virtual Try-Off (VTOFF). VTON generates images of a person in a specified garment using a target photo and a standardized garment image, while a more challenging variant, Person-to-Person Virtual Try-On (p2p-VTON), uses a photo of another person wearing the garment. VTOFF, on the other hand, extracts standardized garment images from clothed individuals. We introduce TryOffDiff, a diffusion-based VTOFF model. Built on a latent diffusion framework with SigLIP image conditioning, it effectively captures garment properties like texture, shape, and patterns. TryOffDiff achieves state-of-the-art results on VITON-HD and strong performance on DressCode dataset, covering upper-body, lower-body, and dresses. Enhanced with class-specific embeddings, it pioneers multi-garment VTOFF, the first of its kind. When paired with VTON models, it improves p2p-VTON by minimizing unwanted attribute transfer, such as skin color. Code is available at: https://rizavelioglu.github.io/tryoffdiff/","url_abs":"https://arxiv.org/abs/2504.13078v1","url_pdf":"https://arxiv.org/pdf/2504.13078v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"enhancing-person-to-person-virtual-try-on","repo_url":"https://github.com/rizavelioglu/tryoffdiff","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"garment-reconstruction","task_name":"Garment Reconstruction"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"virtual-try-off","task_name":"Virtual Try-Off"},{"task_slug":"virtual-try-on","task_name":"Virtual Try-on"}],"methods":[{"method_slug":"adapter","method_name":"Adapter"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"latent-diffusion-model","method_name":"Latent Diffusion Model"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2504.13078","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}