{"type":"ScholarlyArticle","title":"FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on","authors":[{"name":"Chenhui Wang","slug":"chenhui-wang","given":"Chenhui","family":"Wang","affiliation":"Institute of Science and Technology for Brain-inspired Intelligence , MOE Frontiers Center for Brain Science , and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence , Fudan University , Shanghai 200433 , China"},{"name":"Tao Chen","slug":"tao-chen","given":"Tao","family":"Chen","affiliation":"Institute of Science and Technology for Brain-inspired Intelligence , MOE Frontiers Center for Brain Science , and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence , Fudan University , Shanghai 200433 , China"},{"name":"Zhihao Chen","slug":"zhihao-chen","given":"Zhihao","family":"Chen","affiliation":"Institute of Science and Technology for Brain-inspired Intelligence , MOE Frontiers Center for Brain Science , and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence , Fudan University , Shanghai 200433 , China"},{"name":"Zhizhong Huang","slug":"zhizhong-huang","given":"Zhizhong","family":"Huang","affiliation":"School of Computer Science , Fudan University , Shanghai 200433 , China"},{"name":"Taoran Jiang","slug":"taoran-jiang","given":"Taoran","family":"Jiang","affiliation":"Suzhou Xiangji Technology Service Co., Ltd. , Suzhou 215223 , China"},{"name":"Qi Wang","slug":"qi-wang","given":"Qi","family":"Wang","affiliation":"Suzhou Xiangji Technology Service Co., Ltd. , Suzhou 215223 , China"},{"name":"Hongming Shan","slug":"hongming-shan","given":"Hongming","family":"Shan","affiliation":"Institute of Science and Technology for Brain-inspired Intelligence , MOE Frontiers Center for Brain Science , and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence , Fudan University , Shanghai 200433 , China; Shanghai Center for Brain Science and Brain-inspired Technology , Shanghai 200031 , China"}],"abstract":"Despite their impressive generative performance, latent diffusion model-based virtual try-on (VTON) methods lack faithfulness to crucial details of the clothes, such as style, pattern, and text. To alleviate these issues caused by the diffusion stochastic nature and latent supervision, we propose a novel Faithful Latent Diffusion Model for VTON, termed FLDM-VTON. FLDM-VTON improves the conventional latent diffusion process in three major aspects. First, we propose incorporating warped clothes as both the starting point and local condition, supplying the model with faithful clothes priors. Second, we introduce a novel clothes flattening network to constrain generated try-on images, providing clothes-consistent faithful supervision. Third, we devise a clothes-posterior sampling for faithful inference, further enhancing the model performance over conventional clothes-agnostic Gaussian sampling. Extensive experimental results on the benchmark VITON-HD and Dress Code datasets demonstrate that our FLDM-VTON outperforms state-of-the-art baselines and is able to generate photo-realistic try-on images with faithful clothing details.","doi":"10.24963/ijcai.2024/151","url":"https://ijcai.org/proceedings/2024/151","pdf":"https://ijcai.org/proceedings/2024/0151.pdf","markdown":"https://ijcai.org/proceedings/2024/151.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/151","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":151,"pages":{"first":1362,"last":1370},"keywords":["Computer Vision: CV: Image and video synthesis and generation","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bau et al., 2019] David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and An- tonio Torralba. 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