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Latest advances have achieved realistic virtual try-on (VTON) through localized garment inpainting using latent diffusion models, significantly enhancing consumers' online shopping experience. However, existing VTON technologies neglect the…

Computer Vision and Pattern Recognition · Computer Science 2024-08-07 Fei Shen , Xin Jiang , Xin He , Hu Ye , Cong Wang , Xiaoyu Du , Zechao Li , Jinhui Tang

In this paper, we present a diffusion model-based framework for animating people from a single image for a given target 3D motion sequence. Our approach has two core components: a) learning priors about invisible parts of the human body and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-23 Boyi Li , Junming Chen , Jathushan Rajasegaran , Yossi Gandelsman , Alexei A. Efros , Jitendra Malik

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

While recent advances in virtual try-on (VTON) have achieved realistic garment transfer to human subjects, its inverse task, virtual try-off (VTOFF), which aims to reconstruct canonical garment templates from dressed humans, remains…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Angang Zhang , Fang Deng , Hao Chen , Zhongjian Chen , Junyan Li

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…

Computer Vision and Pattern Recognition · Computer Science 2022-04-27 Jianbin Jiang , Tan Wang , He Yan , Junhui Liu

Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these methods…

Computer Vision and Pattern Recognition · Computer Science 2023-12-11 Lihe Ding , Shaocong Dong , Zhanpeng Huang , Zibin Wang , Yiyuan Zhang , Kaixiong Gong , Dan Xu , Tianfan Xue

Text-guided domain adaptation and generation of 3D-aware portraits find many applications in various fields. However, due to the lack of training data and the challenges in handling the high variety of geometry and appearance, the existing…

Computer Vision and Pattern Recognition · Computer Science 2024-04-15 Biwen Lei , Kai Yu , Mengyang Feng , Miaomiao Cui , Xuansong Xie

Video virtual try-on aims to seamlessly replace the clothing of a person in a source video with a target garment. Despite significant progress in this field, existing approaches still struggle to maintain continuity and reproduce garment…

Computer Vision and Pattern Recognition · Computer Science 2025-06-09 Jinjuan Wang , Wenzhang Sun , Ming Li , Yun Zheng , Fanyao Li , Zhulin Tao , Donglin Di , Hao Li , Wei Chen , Xianglin Huang

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Gyeongrok Oh , Jaehwan Jeong , Sieun Kim , Wonmin Byeon , Jinkyu Kim , Sungwoong Kim , Sangpil Kim

Image-based virtual try-on aims to seamlessly fit in-shop clothing to a person image while maintaining pose consistency. Existing methods commonly employ the thin plate spline (TPS) transformation or appearance flow to deform in-shop…

Computer Vision and Pattern Recognition · Computer Science 2025-04-22 Xiaoyu Han , Shunyuan Zheng , Zonglin Li , Chenyang Wang , Xin Sun , Quanling Meng

We present a target-aware video diffusion model that generates videos from an input image, in which an actor interacts with a specified target while performing a desired action. The target is defined by a segmentation mask, and the action…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Taeksoo Kim , Hanbyul Joo

Although image-based virtual try-on has made considerable progress, emerging approaches still encounter challenges in producing high-fidelity and robust fitting images across diverse scenarios. These methods often struggle with issues such…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Boyuan Jiang , Xiaobin Hu , Donghao Luo , Qingdong He , Chengming Xu , Jinlong Peng , Jiangning Zhang , Chengjie Wang , Yunsheng Wu , Yanwei Fu

Generating high-quality 3D objects from textual descriptions remains a challenging problem due to computational cost, the scarcity of 3D data, and complex 3D representations. We introduce Geometry Image Diffusion (GIMDiffusion), a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-09-06 Slava Elizarov , Ciara Rowles , Simon Donné

Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remains a persistent bottleneck. Traditional metrics struggle to quantify fine-grained texture…

Computer Vision and Pattern Recognition · Computer Science 2026-05-07 Jin Li , Tao Chen , Shuai Jiang , Weijie Wang , Jingwen Luo , Chenhui Wu

Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial…

Computer Vision and Pattern Recognition · Computer Science 2022-06-24 Jonathan Ho , Tim Salimans , Alexey Gritsenko , William Chan , Mohammad Norouzi , David J. Fleet

3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Accurately reconstructing an object's complete 3D structure and texture has…

Computer Vision and Pattern Recognition · Computer Science 2024-11-21 Hritam Basak , Hadi Tabatabaee , Shreekant Gayaka , Ming-Feng Li , Xin Yang , Cheng-Hao Kuo , Arnie Sen , Min Sun , Zhaozheng Yin

3D meshes are widely used in computer vision and graphics for their efficiency in animation and minimal memory use, playing a crucial role in movies, games, AR, and VR. However, creating temporally consistent and realistic textures for mesh…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Jingzhi Bao , Xueting Li , Ming-Hsuan Yang

Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting their effectiveness.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-27 Rui Zhang , Yaosen Chen , Yuegen Liu , Wei Wang , Xuming Wen , Hongxia Wang

Existing 4D human datasets fall short for fashion-specific research, lacking either realistic garment dynamics or task-specific annotations. Synthetic datasets suffer from a realism gap, whereas real-world captures lack the detailed…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Hunor Laczkó , Libang Jia , Loc-Phat Truong , Diego Hernández , Sergio Escalera , Jordi Gonzalez , Meysam Madadi

We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Xin Huang , Tengfei Wang , Ziwei Liu , Qing Wang