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We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide…

Computer Vision and Pattern Recognition · Computer Science 2022-03-15 Seunggyu Chang , Jungchan Cho , Songhwai Oh

Seams, distortions, wasted UV space, vertex-duplication, and varying resolution over the surface are the most prominent issues of the standard UV-based texturing of meshes. These issues are particularly acute when automatic UV-unwrapping…

Computer Vision and Pattern Recognition · Computer Science 2024-10-11 Simone Foti , Stefanos Zafeiriou , Tolga Birdal

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

Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR). Although recent advancements in 3D generative models have enhanced content creation, significant…

Computer Vision and Pattern Recognition · Computer Science 2025-01-27 Yunfan Zhang , Zhiwei Xiong , Zhiqi Shen , Guosheng Lin , Hao Wang , Nicolas Vun

We present a generative method for texture filtering, which exhibits surprisingly good performance and generalizability. Our core idea is to empower texture filtering by taking full advantage of the strong learned image prior of pre-trained…

Computer Vision and Pattern Recognition · Computer Science 2026-04-22 Rongjia Zheng , Shangwei Huang , Lei Zhu , Wei-Shi Zheng , Qing Zhang

Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with…

Computer Vision and Pattern Recognition · Computer Science 2023-10-09 An-Chieh Cheng , Xueting Li , Sifei Liu , Xiaolong Wang

Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create…

Computer Vision and Pattern Recognition · Computer Science 2018-03-23 Jiahui Yu , Zhe Lin , Jimei Yang , Xiaohui Shen , Xin Lu , Thomas S. Huang

Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization…

Computer Vision and Pattern Recognition · Computer Science 2024-12-19 Shenhao Zhu , Lingteng Qiu , Xiaodong Gu , Zhengyi Zhao , Chao Xu , Yuxiao He , Zhe Li , Xiaoguang Han , Yao Yao , Xun Cao , Siyu Zhu , Weihao Yuan , Zilong Dong , Hao Zhu

Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation techniques. As such, applying these methods to the volume…

Computer Vision and Pattern Recognition · Computer Science 2023-12-12 Pratul P. Srinivasan , Stephan J. Garbin , Dor Verbin , Jonathan T. Barron , Ben Mildenhall

Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalignment between geometry and texture. To address these…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Huiang He , Shengchu Zhao , Jianwen Huang , Jie Li , Jiaqi Wu , Hu Zhang , Pei Tang , Heliang Zheng , Yukun Li , Rongfei Jia

We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints…

Computer Vision and Pattern Recognition · Computer Science 2025-01-28 Xiaoyu Xiang , Liat Sless Gorelik , Yuchen Fan , Omri Armstrong , Forrest Iandola , Yilei Li , Ita Lifshitz , Rakesh Ranjan

Recovering high-quality 3D facial textures from single-view 2D images is a challenging task, especially under constraints of limited data and complex facial details such as makeup, wrinkles, and occlusions. In this paper, we introduce…

Computer Vision and Pattern Recognition · Computer Science 2025-03-24 Xingchao Yang , Takafumi Taketomi , Yuki Endo , Yoshihiro Kanamori

Recent months have witnessed rapid progress in 3D generation based on diffusion models. Most advances require fine-tuning existing 2D Stable Diffsuions into multi-view settings or tedious distilling operations and hence fall short of 3D…

Computer Vision and Pattern Recognition · Computer Science 2023-12-19 Suyi Jiang , Haimin Luo , Haoran Jiang , Ziyu Wang , Jingyi Yu , Lan Xu

While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new challenges posed by T2MVid generation lie in the lack of…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Bing Li , Cheng Zheng , Wenxuan Zhu , Jinjie Mai , Biao Zhang , Peter Wonka , Bernard Ghanem

As a promising 3D generation technique, multiview diffusion (MVD) has received a lot of attention due to its advantages in terms of generalizability, quality, and efficiency. By finetuning pretrained large image diffusion models with 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Xin-Yang Zheng , Hao Pan , Yu-Xiao Guo , Xin Tong , Yang Liu

Generating high-fidelity, 3D-consistent garment textures remains a challenging problem due to the inherent complexities of garment structures and the stringent requirement for detailed, globally consistent texture synthesis. Existing…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Jinbo Wu , Xiaobo Gao , Xing Liu , Chen Zhao , Jialun Liu

High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a…

Computer Vision and Pattern Recognition · Computer Science 2025-09-08 Arianna Rampini , Kanika Madan , Bruno Roy , AmirHossein Zamani , Derek Cheung

Given an incomplete image without additional constraint, image inpainting natively allows for multiple solutions as long as they appear plausible. Recently, multiplesolution inpainting methods have been proposed and shown the potential of…

Computer Vision and Pattern Recognition · Computer Science 2021-03-19 Jialun Peng , Dong Liu , Songcen Xu , Houqiang Li

Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle…

Computer Vision and Pattern Recognition · Computer Science 2024-08-05 Dong Huo , Zixin Guo , Xinxin Zuo , Zhihao Shi , Juwei Lu , Peng Dai , Songcen Xu , Li Cheng , Yee-Hong Yang

We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object. Our architecture unifies the concepts of (i) multi-view super-resolution…

Computer Vision and Pattern Recognition · Computer Science 2020-01-15 Audrey Richard , Ian Cherabier , Martin R. Oswald , Vagia Tsiminaki , Marc Pollefeys , Konrad Schindler