{"title":"自监督功能匹配虚拟试穿","authors":"Shiyi Jiang, Yang Xu, Danyang Li, Runze Fan","doi":"10.1093/jcde/qwad085","DOIUrl":null,"url":null,"abstract":"Abstract Virtual try-on is a technology that enables users to preview the effect of wearing a target garment without wearing the actual garment. However, existing image-based virtual try-on methods often require additional human parsing or segmentation operations to generate intermediate representations required for garment deformation and texture fusion. These operations not only increase the computational complexity and memory consumption, but also limit the real-time and portability of virtual try-on. Additionally, inaccurate parsing results can lead to misleading final generated images. To overcome these challenges, we propose a self-supervised feature matched virtual try-on network, which can directly generate high-quality try-on results from human body images and target clothing images without any additional input. Specifically, we design an optical flow warp module, which focuses on the optical flow changes between the person image and the clothing image to achieve accurate clothing alignment and deformation. Furthermore, a feature refine warp module is designed to enhance the features of the extracted optical flow information and the original character segmentation and analysis operations, reducing the influence of background clutter features on the content, and ensuring that the wrinkles and deformation of the replacement clothes are close to the original clothes. The feature match module is developed to calculate the feature matching loss of the converted clothing and the generated results of the teacher network and the student network, and the corresponding knowledge is distilled and passed to the student network to assist in self-supervised training. We conduct experiments on the VITON dataset and show that our model can generate high quality and high resolution, and our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-supervised feature matched virtual try-on\",\"authors\":\"Shiyi Jiang, Yang Xu, Danyang Li, Runze Fan\",\"doi\":\"10.1093/jcde/qwad085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Virtual try-on is a technology that enables users to preview the effect of wearing a target garment without wearing the actual garment. However, existing image-based virtual try-on methods often require additional human parsing or segmentation operations to generate intermediate representations required for garment deformation and texture fusion. These operations not only increase the computational complexity and memory consumption, but also limit the real-time and portability of virtual try-on. Additionally, inaccurate parsing results can lead to misleading final generated images. To overcome these challenges, we propose a self-supervised feature matched virtual try-on network, which can directly generate high-quality try-on results from human body images and target clothing images without any additional input. Specifically, we design an optical flow warp module, which focuses on the optical flow changes between the person image and the clothing image to achieve accurate clothing alignment and deformation. Furthermore, a feature refine warp module is designed to enhance the features of the extracted optical flow information and the original character segmentation and analysis operations, reducing the influence of background clutter features on the content, and ensuring that the wrinkles and deformation of the replacement clothes are close to the original clothes. The feature match module is developed to calculate the feature matching loss of the converted clothing and the generated results of the teacher network and the student network, and the corresponding knowledge is distilled and passed to the student network to assist in self-supervised training. We conduct experiments on the VITON dataset and show that our model can generate high quality and high resolution, and our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.\",\"PeriodicalId\":48611,\"journal\":{\"name\":\"Journal of Computational Design and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Design and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/jcde/qwad085\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jcde/qwad085","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Abstract Virtual try-on is a technology that enables users to preview the effect of wearing a target garment without wearing the actual garment. However, existing image-based virtual try-on methods often require additional human parsing or segmentation operations to generate intermediate representations required for garment deformation and texture fusion. These operations not only increase the computational complexity and memory consumption, but also limit the real-time and portability of virtual try-on. Additionally, inaccurate parsing results can lead to misleading final generated images. To overcome these challenges, we propose a self-supervised feature matched virtual try-on network, which can directly generate high-quality try-on results from human body images and target clothing images without any additional input. Specifically, we design an optical flow warp module, which focuses on the optical flow changes between the person image and the clothing image to achieve accurate clothing alignment and deformation. Furthermore, a feature refine warp module is designed to enhance the features of the extracted optical flow information and the original character segmentation and analysis operations, reducing the influence of background clutter features on the content, and ensuring that the wrinkles and deformation of the replacement clothes are close to the original clothes. The feature match module is developed to calculate the feature matching loss of the converted clothing and the generated results of the teacher network and the student network, and the corresponding knowledge is distilled and passed to the student network to assist in self-supervised training. We conduct experiments on the VITON dataset and show that our model can generate high quality and high resolution, and our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.