We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy. Conditioned upon a new clothing-agnostic yet descriptive person representation, our framework first generates a coarse synthesized image with the target clothing item overlaid on that same person in the same pose. We further enhance the initial blurry clothing area with a refinement network. The network is trained to learn how much detail to utilize from the target clothing item, and where to apply to the person in order to synthesize a photo-realistic image in which the target item deforms naturally with clear visual patterns. Experiments on our newly collected Zalando dataset demonstrate its promise in the image-based virtual try-on task over state-of-the-art generative models.
Image-based virtual try-on provides the capacity to transfer a clothing item onto a photo of a given person, which is usually accomplished by warping the item to a given human pose and adjusting the warped item to the person. However, the results of real-world synthetic images (e.g., selfies) from the previous method is not realistic because of the limitations which result in the neck being misrepresented and significant changes to the style of the garment. To address these challenges, we propose a novel method to solve this unique issue, called VITON-CROP. VITON-CROP synthesizes images more r…
Electronics and Telecommunications Research Institute（电子通信研究院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
Studies of virtual try-on (VITON) have been shown their effectiveness in utilizing the generative neural network for virtually exploring fashion products, and some of recent researches of VITON attempted to synthesize human image wearing given multiple types of garments (e.g., top and bottom clothes). However, when replacing the top and bottom clothes of the target human, numerous wearing styles are possible with a certain combination of the clothes. In this paper, we address the problem of variation in wearing style when simultaneously replacing the top and bottom clothes of the model. We int…
The University of Tokyo（东京大学）；The International Research Center for Neurointelligence（国际神经智能研究中心）
Generating a virtual try-on image from in-shop clothing images and a model person's snapshot is a challenging task because the human body and clothes have high flexibility in their shapes. In this paper, we develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images using images of in-shop clothing and a model person. This method enhances the quality of the generated image when occlusion is present in a model person's image (e.g., arms crossed in front of the clothes) by adding an adversarial mechanism in the training pipeline.
The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthesized images is still limited to low (e.g., 256x192), which acts as the critical limitation against satisfying online consumers. We argue that the limitation stems from several challenges: as the resolution increases, the artifacts in the misaligned areas between the warped cloth…
Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusio…
Previous virtual try-on methods usually focus on aligning a clothing item with a person, limiting their ability to exploit the complex pose, shape and skin color of the person, as well as the overall structure of the clothing, which is vital to photo-realistic virtual try-on. To address this potential weakness, we propose a fill in fabrics (FIFA) model, a self-supervised conditional generative adversarial network based framework comprised of a Fabricator and a unified virtual try-on pipeline with a Segmenter, Warper and Fuser. The Fabricator aims to reconstruct the clothing image when provided…
FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Suzhou Xiangji Technology Service Co., Ltd.（苏州湘记科技服务有限公司）
South China University of Technology（华南理工大学）；Nanyang Technological University（南洋理工大学）；Guangdong University of Technology（广东工业大学）
With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virtual try-on while it is important in real-world applications. Most existing video-based virtual try-on methods usually require clothing templates and they can only generate blurred and low-resolution results. To address these challenges, we propose a Memory-based Video virtual Try-On Network (MV-TON), which seamlessly transfers desired clothes to a target person without using any clothing templates and generates high-…
The University of Hong Kong（香港大学）；Tencent AI Lab（腾讯AI实验室）；ETH Zürich（苏黎世联邦理工学院）；Tencent Data Platform（腾讯数据平台）
Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation networks to reconstruct the input image in a self-supervised manner. However, existing methods do not differentiate clothing and non-clothing regions. A straight-forward generation impedes virtual try-on quality because of the heavily coupled image contents. In this paper, we propose …
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
Machine Learning Group, CITEC Bielefeld University Germany（比勒菲尔德大学机器学习小组、CITEC）；Institute of Mathematics Technical University Berlin Germany（柏林技术大学数学研究所）
Hierarchical Cross-Attention Network for Virtual Try-On
School of Computer Science, Peking University（北京大学计算机学院）；University of Trento（特伦托大学）；School of Engineering Audit, Nanjing Audit University（南京审计大学工程审计学院）
ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset
RPTU Kaiserslautern-Landau（凯泽斯劳滕-兰道莱茵兰-普法尔茨技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Shapematchr GmbH（Shapematchr有限公司）
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
Harbin Institute of Technology（哈尔滨工业大学）；HiDream.ai Inc.（HiDream.ai公司）；Harbin Institute of Technology (Weihai) Qingdao Research Institute（哈尔滨工业大学（威海）青岛研究院）
MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Khalifa University（哈利法大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Jio Institute（Jio研究所）
The virtual try-on system has gained great attention due to its potential to give customers a realistic, personalized product presentation in virtualized settings. In this paper, we present PT-VTON, a novel pose-transfer-based framework for cloth transfer that enables virtual try-on with arbitrary poses. PT-VTON can be applied to the fashion industry within minimal modification of existing systems while satisfying the overall visual fashionability and detailed fabric appearance requirements. It enables efficient clothes transferring between model and user images with arbitrary pose and body sh…
With the increasing development of garment manufacturing industry, the method of combining neural network with industry to reduce product redundancy has been paid more and more attention.In order to reduce garment redundancy and achieve personalized customization, more researchers have appeared in the field of virtual trying on.They try to transfer the target clothing to the reference figure, and then stylize the clothes to meet user's requirements for fashion.But the biggest problem of virtual try on is that the shape and motion blocking distort the clothes, causing the patterns and texture o…
Image virtual try-on task has abundant applications and has become a hot research topic recently. Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect. In this paper, we put forward an akin task that aims to dress clothing for underwear models. %, which is also an urgent need in e-commerce scenarios. To solve the above drawback…
The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual try-on, they lead to inconsistency between frames when applied to videos. Limited work also explored the task of video-based virtual try-on but failed to produce visually pleasing and temporally coherent results. Moreover, there are two other key challenges: 1) how to generate accurate warping when occlusions appear in the clothing region; 2) how to generate clothes and non-target body parts (e.g. arms, neck) in harmony with the…
This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achiev…
Sun Yat-Sen University（中山大学）；UiT The Arctic University of Norway（挪威北极大学）；Momo Technology Company Limited（陌陌科技有限公司）；DarkMatter AI Research（暗物质人工智能研究院）
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations. In this paper, we address this problem by finding clothing category-specific warping networks for the virtual try-on task via Neural Architecture Search (NAS). We introduce a NAS-Warping Module and elaborately design a bilevel hierarchical search space to identify the optimal network-level and operation-level flow estimation architecture. Given the networ…
Image-based virtual try-on is challenging in fitting a target in-shop clothes into a reference person under diverse human poses. Previous works focus on preserving clothing details ( e.g., texture, logos, patterns ) when transferring desired clothes onto a target person under a fixed pose. However, the performances of existing methods significantly dropped when extending existing methods to multi-pose virtual try-on. In this paper, we propose an end-to-end Semantic Prediction Guidance multi-pose Virtual Try-On Network (SPG-VTON), which could fit the desired clothing into a reference person und…
IPVTON: Image-based 3D Virtual Try-on with Image Prompt Adapter
School of Software Engineering, South China University of Technology（华南理工大学软件学院）；Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Peng Cheng Laboratory（鹏城实验室）
Shenzhen Campus of SYSU（中山大学深圳校区）；UiT The Arctic University of Norway（挪威北极大学）；UC San Diego（加利福尼亚大学圣迭戈分校）；Momo（陌陌科技）
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value. However, existing 3D virtual try-on methods mainly rely on annotated 3D human shapes and garment templates, which hinders their applications in practical scenarios. 2D virtual try-on approaches provide a faster alternative to manipulate clothed humans, but lack the rich and realistic 3D representation. In this paper, we propose a novel Monocular-to-3D Virtual Try-On Network (M3D-VTON) that builds on the merits of both 2D and 3D approaches. By integrating 2D information e…
The conventional image-based virtual try-on method cannot generate fitting images that correspond to the clothing size because the system cannot accurately reflect the body information of a person. In this study, an image-based virtual try-on system that could adjust the clothing size was proposed. The size information of the person and clothing were used as the input for the proposed method to visualize the fitting of various clothing sizes in a virtual space. First, the distance between the shoulder width and height of the clothing in the person image is calculated based on the coordinate in…
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Carnegie Mellon University（卡内基梅隆大学）；UiT The Arctic University of Norway（挪威北极大学）；ByteDance（字节跳动）
Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing. To this end, we propose a characteristic-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive GAN++ (PASTA-GAN++), to achieve a versatile system for high-resolution unpaired virtual try-…
TalkFashion: Intelligent Virtual Try-On Assistant Based on Multimodal Large Language Model
School of Electronic and Computer Engineering, Peking University, China（北京大学电子与计算机工程学院）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University（北京大学深圳研究生院）
Sun Yat-sen University（中山大学）；South China Normal University（华南师范大学）
Virtual try-on system under arbitrary human poses has huge application potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy misalignment among diverse poses, and diverse clothes textures. Existing methods aim at fitting new clothes into a person can only transfer clothes on the fixed human pose, but still show unsatisfactory performances which often fail to preserve the identity, lose the texture details, and decrease the diversity of poses. In this paper, we make the first attempt towards multi-pose guided virtual try-on system, which enables transfer clothes on a pers…
