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Related papers: WildVidFit: Video Virtual Try-On in the Wild via I…

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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…

Computer Vision and Pattern Recognition · Computer Science 2021-07-29 Xin Gao , Zhenjiang Liu , Zunlei Feng , Chengji Shen , Kairi Ou , Haihong Tang , Mingli Song

We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent information rich in prior information to capture the nuances of…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Wengyi Zhan , Mingbao Lin , Shuicheng Yan , Rongrong Ji

The garment-to-person virtual try-on (VTON) task, which aims to generate fitting images of a person wearing a reference garment, has made significant strides. However, obtaining a standard garment is often more challenging than using the…

Computer Vision and Pattern Recognition · Computer Science 2025-02-04 Le Shen , Yanting Kang , Rong Huang , Zhijie Wang

We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view rendering, and diverse garment choices. Unlike existing…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Zicheng Jiang , Jixin Gao , Shengfeng He , Xinzhe Li , Yulong Zheng , Zhaotong Yang , Junyu Dong , Yong Du

We introduce a framework that enables both multi-view character consistency and 3D camera control in video diffusion models through a novel customization data pipeline. We train the character consistency component with recorded volumetric…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Yuancheng Xu , Wenqi Xian , Li Ma , Julien Philip , Ahmet Levent Taşel , Yiwei Zhao , Ryan Burgert , Mingming He , Oliver Hermann , Oliver Pilarski , Rahul Garg , Paul Debevec , Ning Yu

Virtual try-on is a promising application of computer graphics and human computer interaction that can have a profound real-world impact especially during this pandemic. Existing image-based works try to synthesize a try-on image from a…

Graphics · Computer Science 2021-09-13 Toby Chong , I-Chao Shen , Nobuyuki Umetani , Takeo Igarashi

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…

Computer Vision and Pattern Recognition · Computer Science 2018-06-14 Xintong Han , Zuxuan Wu , Zhe Wu , Ruichi Yu , Larry S. Davis

Virtual try-on seeks to generate photorealistic images of individuals in desired garments, a task that must simultaneously preserve personal identity and garment fidelity for practical use in fashion retail and personalization. However,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-13 Ankan Deria , Dwarikanath Mahapatra , Behzad Bozorgtabar , Mohna Chakraborty , Snehashis Chakraborty , Sudipta Roy

The aim of image-based virtual try-on is to generate realistic images of individuals wearing target garments, ensuring that the pose, body shape and characteristics of the target garment are accurately preserved. Existing methods often fail…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Maliheh Toozandehjani , Ali Mousavi , Reza Taheri

Virtual try-on (VTON) has been widely explored for rendering garments onto person images, while its inverse task, virtual try-off (VTOFF), remains largely overlooked. VTOFF aims to recover standardized product images of garments directly…

Computer Vision and Pattern Recognition · Computer Science 2026-02-24 Davide Lobba , Fulvio Sanguigni , Bin Ren , Marcella Cornia , Rita Cucchiara , Nicu Sebe

Although recent text-to-video generative models are getting more capable of following external camera controls, imposed by either text descriptions or camera trajectories, they still struggle to generalize to unconventional camera motions,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-30 Qiucheng Wu , Handong Zhao , Zhixin Shu , Jing Shi , Yang Zhang , Shiyu Chang

Text-to-video diffusion models have advanced video generation significantly. However, customizing these models to generate videos with tailored motions presents a substantial challenge. In specific, they encounter hurdles in (a) accurately…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Hyeonho Jeong , Geon Yeong Park , Jong Chul Ye

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.…

Computer Vision and Pattern Recognition · Computer Science 2021-08-18 Xiaojing Zhong , Zhonghua Wu , Taizhe Tan , Guosheng Lin , Qingyao Wu

Virtual Try-On (VTON) technology allows users to visualize how clothes would look on them without physically trying them on, gaining traction with the rise of digitalization and online shopping. Traditional VTON methods, often using…

Computer Vision and Pattern Recognition · Computer Science 2025-01-14 Seohyun Lee , Jintae Park , Sanghyeok Park

We present FloodDiffusion, a new framework for text-driven, streaming human motion generation. Given time-varying text prompts, FloodDiffusion generates text-aligned, seamless motion sequences with real-time latency. Unlike existing methods…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Yiyi Cai , Yuhan Wu , Kunhang Li , You Zhou , Bo Zheng , Haiyang Liu

Diffusion-based methods can generate realistic images and videos, but they struggle to edit existing objects in a video while preserving their appearance over time. This prevents diffusion models from being applied to natural video editing…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Wenhao Chai , Xun Guo , Gaoang Wang , Yan Lu

Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these problems, in this paper, we view frames as continuous functions…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Giannis Daras , Weili Nie , Karsten Kreis , Alex Dimakis , Morteza Mardani , Nikola Borislavov Kovachki , Arash Vahdat

Image-based virtual try-on aims to transfer target in-shop clothing to a dressed model image, the objectives of which are totally taking off original clothing while preserving the contents outside of the try-on area, naturally wearing…

Computer Vision and Pattern Recognition · Computer Science 2024-09-24 Dan Song , Xuanpu Zhang , Jianhao Zeng , Pengxin Zhan , Qingguo Chen , Weihua Luo , An-An Liu

The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Wenqi Ouyang , Yi Dong , Lei Yang , Jianlou Si , Xingang Pan

Infrared and visible video fusion is essential for achieving comprehensive perception in dynamic scenes. However, maintaining temporal consistency remains a formidable challenge. Conventional methods relying on optical flow often suffer…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Xingyuan Li , Haoyuan Xu , Shulin Li , Xiang Chen , Zhiying Jiang , Jinyuan Liu
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