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Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3D voxels remains prohibitively expensive due to the cubic…

Computer Vision and Pattern Recognition · Computer Science 2023-12-13 Shentong Mo , Enze Xie , Yue Wu , Junsong Chen , Matthias Nießner , Zhenguo Li

Virtual try-on systems have long been hindered by heavy reliance on human body masks, limited fine-grained control over garment attributes, and poor generalization to real-world, in-the-wild scenarios. In this paper, we propose JCo-MVTON…

Computer Vision and Pattern Recognition · Computer Science 2025-08-26 Aowen Wang , Wei Li , Hao Luo , Mengxing Ao , Chenyu Zhu , Xinyang Li , Fan Wang

While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-25 Xin Yu , Ze Yuan , Yuan-Chen Guo , Ying-Tian Liu , JianHui Liu , Yangguang Li , Yan-Pei Cao , Ding Liang , Xiaojuan Qi

3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Lukas Höllein , Aljaž Božič , Norman Müller , David Novotny , Hung-Yu Tseng , Christian Richardt , Michael Zollhöfer , Matthias Nießner

We present a method to dynamically deform 3D garments, in the form of a 3D polygon mesh, based on body shape, motion, and physical cloth material properties. Considering physical cloth properties allows to learn a physically grounded model,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-09 Antoine Dumoulin , Adnane Boukhayma , Laurence Boissieux , Bharath Bhushan Damodaran , Pierre Hellier , Stefanie Wuhrer

Style-guided texture generation aims to generate a texture that is harmonious with both the style of the reference image and the geometry of the input mesh, given a reference style image and a 3D mesh with its text description. Although…

Computer Vision and Pattern Recognition · Computer Science 2024-11-04 Zhiyu Xie , Yuqing Zhang , Xiangjun Tang , Yiqian Wu , Dehan Chen , Gongsheng Li , Xaogang Jin

Virtual Try-on (VTON) has become a core capability for online retail, where realistic try-on results provide reliable fit guidance, reduce returns, and benefit both consumers and merchants. Diffusion-based VTON methods achieve…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Haohua Chen , Tianze Zhou , Wei Zhu , Runqi Wang , Yandong Guan , Dejia Song , Yibo Chen , Xu Tang , Yao Hu , Lu Sheng , Zhiyong Wu

Recent advances in diffusion models bring new vitality to visual content creation. However, current text-to-video generation models still face significant challenges such as high training costs, substantial data requirements, and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Sicong Feng , Jielong Yang , Li Peng

We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high…

Computer Vision and Pattern Recognition · Computer Science 2023-03-22 Dave Zhenyu Chen , Yawar Siddiqui , Hsin-Ying Lee , Sergey Tulyakov , Matthias Nießner

Video Virtual Try-on aims to seamlessly transfer a reference garment onto a target person in a video while preserving both visual fidelity and temporal coherence. Existing methods typically rely on inpainting masks to define the try-on…

Computer Vision and Pattern Recognition · Computer Science 2025-07-22 Tianyu Chang , Xiaohao Chen , Zhichao Wei , Xuanpu Zhang , Qing-Guo Chen , Weihua Luo , Peipei Song , Xun Yang

Recent advances in the diffusion models have significantly improved text-to-image generation. However, generating videos from text is a more challenging task than generating images from text, due to the much larger dataset and higher…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Taegyeong Lee , Soyeong Kwon , Taehwan Kim

Latent diffusion models for image generation have crossed a quality threshold which enabled them to achieve mass adoption. Recently, a series of works have made advancements towards replicating this success in the 3D domain, introducing…

Computer Vision and Pattern Recognition · Computer Science 2023-03-29 Anchit Gupta , Wenhan Xiong , Yixin Nie , Ian Jones , Barlas Oğuz

Diffusion-based methods have achieved prominent success in generating 2D media. However, accomplishing similar proficiencies for scene-level mesh texturing in 3D spatial applications, e.g., XR/VR, remains constrained, primarily due to the…

Computer Vision and Pattern Recognition · Computer Science 2023-10-23 Bangbang Yang , Wenqi Dong , Lin Ma , Wenbo Hu , Xiao Liu , Zhaopeng Cui , Yuewen Ma

Garment sewing patterns are fundamental design elements that bridge the gap between design concepts and practical manufacturing. The generative modeling of sewing patterns is crucial for creating diversified garments. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Xinyu Li , Qi Yao , Yuanda Wang

Recent advances in diffusion-based text-to-video (T2V) models have demonstrated remarkable progress, but these models still face challenges in generating videos with multiple objects. Most models struggle with accurately capturing complex…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Aimon Rahman , Jiang Liu , Ze Wang , Ximeng Sun , Jialian Wu , Xiaodong Yu , Yusheng Su , Vishal M. Patel , Zicheng Liu , Emad Barsoum

3D style transfer enables the creation of visually expressive 3D content, enriching the visual appearance of 3D scenes and objects. However, existing VGG- and CLIP-based methods struggle to model multi-view consistency within the model…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Yitong Yang , Xuexin Liu , Yinglin Wang , Jing Wang , Hao Dou , Changshuo Wang , Shuting He

Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garment detail preservation, driven by powerful text-to-image (T2I) diffusion models. However, existing methods often rely on user-provided masks,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Zhenchen Wan , Yanwu xu , Dongting Hu , Weilun Cheng , Tianxi Chen , Zhaoqing Wang , Feng Liu , Tongliang Liu , Mingming Gong

3D generation methods have shown visually compelling results powered by diffusion image priors. However, they often fail to produce realistic geometric details, resulting in overly smooth surfaces or geometric details inaccurately baked in…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Ruihan Gao , Kangle Deng , Gengshan Yang , Wenzhen Yuan , Jun-Yan Zhu

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

Recent diffusion methods have made significant progress in generating videos from single images due to their powerful visual generation capabilities. However, challenges persist in image-to-video synthesis, particularly in human video…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Tiantian Wang , Chun-Han Yao , Tao Hu , Mallikarjun Byrasandra Ramalinga Reddy , Ming-Hsuan Yang , Varun Jampani