提出 VTON-IT，结合语义分割与基于 GAN 的图像转换网络，将目标衣物叠加到分割身体区域，在真实多变图像上生成对齐、高分辨率且纹理细腻的试穿结果。
摘要：
虚拟试穿（在虚拟环境中试穿衣物）是生成对抗网络（GAN）的一项很有前景的应用。然而，由于体型、姿势各异，以及头发和衣物重叠等遮挡因素，将期望的服装单品迁移到人体相应区域是一项艰巨任务。本文尝试通过语义分割和基于生成对抗架构的图像转换网络，生成照片级真实的转换图像。我们提出了一种新颖的基于图像的虚拟试穿应用 VTON-IT，它接收 RGB 图像，分割期望的身体部位，并将目标衣物叠加到分割出的身体区域上。大多数最先进的基于 GAN 的虚拟试穿应用在真实测试图像上会产生未对齐、像素化的合成图像；而我们的方法在此类多变图像上能够生成具有细腻纹理的高分辨率自然图像。
英文摘要：
Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN). However, it is an arduous task to transfer the desired clothing item onto the corresponding regions of a human body because of varying body size, pose, and occlusions like hair and overlapped clothes. In this paper, we try to produce photo-realistic translated images through semantic segmentation and a generative adversarial architecture-based image translation network. We present a novel image-based Virtual Try-On application VTON-IT that takes an RGB image, segments desired body part, and overlays target cloth over the segmented body region. Most state-of-the-art GAN-based Virtual Try-On applications produce unaligned pixelated synthesis images on real-life test images. However, our approach generates high-resolution natural images with detailed textures on such variant images.
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）
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…
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）
Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person at a target pose. Compared to traditional virtual try-on (VTON) that fits the garment but keeps the pose unchanged, MPVTON provides a better try-on experience, but is also more challenging due to the dual garment and pose editing objectives. Existing MPVTON methods adopt a pipeline comprising three disjoint modules including a target semantic layout prediction module, a coarse try-on image generator and a refinement try-on image generator. These models are trained separately, leading to sub-optimal model training and…
Neural Clothing Tryer: Customized Virtual Try-On via Semantic Enhancement and Controlling Diffusion Model
Guangdong University of Technology, Guangzhou 510006, China；Guangdong Provincial Key Laboratory of Intellectual Property \& Big Data, Guangzhou 510006, China；South China University of Technology, Guangzhou 510006, China；Genstoraige Technology (Beijing) Co., Ltd, Beijing 100000, China
DreamVTON: Customizing 3D Virtual Try-on with Personalized Diffusion Models
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；ByteDance（字节跳动）；Carnegie Mellon University（卡内基梅隆大学）；Research Institute of Multiple Agents and Embodied Intelligence（多智能体与具身智能研究院）；Peng Cheng Laboratory（鹏城实验室）
本文提出 DreamVTON 模型，通过结合个性化扩散模型、Densepose-guided ControlNet 和基于模板的优化机制，分别优化 3D 人体的几何与纹理，解决了个性化模块削弱多视图合成能力的问题，实现了高质量的定制化 3D 虚拟试穿。
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…
San Jose State University（圣何塞州立大学）；Carnegie Mellon University（卡内基梅隆大学）；Nanyang Technological University（南洋理工大学）
Virtual try-on has garnered interest as a neural rendering benchmark task to evaluate complex object transfer and scene composition. Recent works in virtual clothing try-on feature a plethora of possible architectural and data representation choices. However, they present little clarity on quantifying the isolated visual effect of each choice, nor do they specify the hyperparameter details that are key to experimental reproduction. Our work, ShineOn, approaches the try-on task from a bottom-up approach and aims to shine light on the visual and quantitative effects of each experiment. We build …
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…
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…
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…
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…
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 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.
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）
