{"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/style-based-global-appearance-flow-for","title":"Style-Based Global Appearance Flow for Virtual Try-On","arxiv_id":"2204.01046","date":"2022-04-03","proceeding":"CVPR 2022 1","authors":["Sen He","Yi-Zhe Song","Tao Xiang"],"abstract":"Image-based virtual try-on aims to fit an in-shop garment into a clothed person image. To achieve this, a key step is garment warping which spatially aligns the target garment with the corresponding body parts in the person image. Prior methods typically adopt a local appearance flow estimation model. They are thus intrinsically susceptible to difficult body poses/occlusions and large mis-alignments between person and garment images (see Fig.~\\ref{fig:fig1}). To overcome this limitation, a novel global appearance flow estimation model is proposed in this work. For the first time, a StyleGAN based architecture is adopted for appearance flow estimation. This enables us to take advantage of a global style vector to encode a whole-image context to cope with the aforementioned challenges. To guide the StyleGAN flow generator to pay more attention to local garment deformation, a flow refinement module is introduced to add local context. Experiment results on a popular virtual try-on benchmark show that our method achieves new state-of-the-art performance. It is particularly effective in a `in-the-wild' application scenario where the reference image is full-body resulting in a large mis-alignment with the garment image (Fig.~\\ref{fig:fig1} Top). Code is available at: \\url{https://github.com/SenHe/Flow-Style-VTON}.","url_abs":"https://arxiv.org/abs/2204.01046v1","url_pdf":"https://arxiv.org/pdf/2204.01046v1.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":"style-based-global-appearance-flow-for","repo_url":"https://github.com/senhe/flow-style-vton","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"style-based-global-appearance-flow-for","repo_url":"https://github.com/KiseKloset/DM-VTON","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"style-based-global-appearance-flow-for","repo_url":"https://github.com/MosbehBarhoumi/VITON-PRE-PROCESSING","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"virtual-try-on","task_name":"Virtual Try-on"}],"methods":[{"method_slug":"adaptive-instance-normalization","method_name":"Adaptive Instance Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"r1-regularization","method_name":"R1 Regularization"},{"method_slug":"stylegan","method_name":"StyleGAN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/virtual-try-on-on-viton","task":"Virtual Try-on","dataset":"VITON","model":"Flow-Style-VTON","rank_in_archive_order":1,"of":10,"metrics":{"FID":"8.89","SSIM":"0.91"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.01046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01046"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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