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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

Automatic 3D content creation has gained increasing attention recently, due to its potential in various applications such as video games, film industry, and AR/VR. Recent advancements in diffusion models and multimodal models have notably…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Yitong Wang , Xudong Xu , Li Ma , Haoran Wang , Bo Dai

Recently, significant advances have been made in 3D object generation. Building upon the generated geometry, current pipelines typically employ image diffusion models to generate multi-view RGB images, followed by UV texture reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Mingqi Shao , Feng Xiong , Zhaoxu Sun , Mu Xu

We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared to works that bake shading in the 3D object's appearance,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-03 Yawar Siddiqui , Tom Monnier , Filippos Kokkinos , Mahendra Kariya , Yanir Kleiman , Emilien Garreau , Oran Gafni , Natalia Neverova , Andrea Vedaldi , Roman Shapovalov , David Novotny

In this paper, we introduce MeshGen, an advanced image-to-3D pipeline that generates high-quality 3D meshes with detailed geometry and physically based rendering (PBR) textures. Addressing the challenges faced by existing 3D native…

Graphics · Computer Science 2025-05-09 Zilong Chen , Yikai Wang , Wenqiang Sun , Feng Wang , Yiwen Chen , Huaping Liu

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

Existing industrial 3D garment meshes already cover most real-world clothing geometries, yet their texture diversity remains limited. To acquire more realistic textures, generative methods are often used to extract Physically-based…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Hui Shan , Ming Li , Haitao Yang , Kai Zheng , Sizhe Zheng , Yanwei Fu , Xiangru Huang

The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture…

Computer Vision and Pattern Recognition · Computer Science 2024-07-03 Raphael Bensadoun , Yanir Kleiman , Idan Azuri , Omri Harosh , Andrea Vedaldi , Natalia Neverova , Oran Gafni

Generating high-quality physically based rendering (PBR) materials is important to achieve realistic rendering in the downstream tasks, yet it remains challenging due to the intertwined effects of materials and lighting. While existing…

Computer Vision and Pattern Recognition · Computer Science 2025-03-17 Xiaokang Wei , Bowen Zhang , Xianghui Yang , Yuxuan Wang , Chunchao Guo , Xi Zhao , Yan Luximon

Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, none of these is able to produce textured objects, which renders them of limited use for practical tasks. In this work, we present the first…

Computer Vision and Pattern Recognition · Computer Science 2020-04-10 Paul Henderson , Vagia Tsiminaki , Christoph H. Lampert

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train…

Computer Vision and Pattern Recognition · Computer Science 2022-09-23 Jun Gao , Tianchang Shen , Zian Wang , Wenzheng Chen , Kangxue Yin , Daiqing Li , Or Litany , Zan Gojcic , Sanja Fidler

Novel photo-realistic texture synthesis is an important task for generating novel scenes, including asset generation for 3D simulations. However, to date, these methods predominantly generate textured objects in 2D space. If we rely on 2D…

Computer Vision and Pattern Recognition · Computer Science 2023-02-09 Dharma KC , Clayton T. Morrison , Bradley Walls

Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. However, restricted control flexibility and limited prompt…

Graphics · Computer Science 2025-06-04 Dongyu Yan , Leyi Wu , Jiantao Lin , Luozhou Wang , Tianshuo Xu , Zhifei Chen , Zhen Yang , Lie Xu , Shunsi Zhang , Yingcong Chen

The influence of textures on machine learning models has been an ongoing investigation, specifically in texture bias/learning, interpretability, and robustness. However, due to the lack of large and diverse texture data available, the…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Blaine Hoak , Patrick McDaniel

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

We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are…

Graphics · Computer Science 2023-04-18 Zhen Liu , Yao Feng , Michael J. Black , Derek Nowrouzezahrai , Liam Paull , Weiyang Liu

Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for…

Computer Vision and Pattern Recognition · Computer Science 2025-07-31 Zebin He , Mingxin Yang , Shuhui Yang , Yixuan Tang , Tao Wang , Kaihao Zhang , Guanying Chen , Yuhong Liu , Jie Jiang , Chunchao Guo , Wenhan Luo

Learning to generate textures for a novel 3D mesh given a collection of 3D meshes and real-world 2D images is an important problem with applications in various domains such as 3D simulation, augmented and virtual reality, gaming,…

Computer Vision and Pattern Recognition · Computer Science 2024-03-08 Dharma KC , Clayton T. Morrison

While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challenge in the 3D texture generation domain is that most existing…

Computer Vision and Pattern Recognition · Computer Science 2025-12-10 AmirHossein Zamani , Tianhao Xie , Amir G. Aghdam , Tiberiu Popa , Eugene Belilovsky

Physically-based rendering (PBR) provides a principled standard for realistic material-lighting interactions in computer graphics. Despite recent advances in generating PBR textures, existing methods fail to address two fundamental…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Jingzhi Bao , Hongze Chen , Lingting Zhu , Chenyu Liu , Runze Zhang , Keyang Luo , Zeyu Hu , Weikai Chen , Yingda Yin , Xin Wang , Zehong Lin , Jun Zhang , Xiaoguang Han
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