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Related papers: CraftsMan3D: High-fidelity Mesh Generation with 3D…

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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

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

Controllable, high-fidelity mesh editing remains a significant challenge in 3D content creation. Existing generative methods often struggle with complex geometries and fail to produce detailed results. We propose CraftMesh, a novel…

Graphics · Computer Science 2026-03-31 James Jincheng , Yuxiao Wu , Youcheng Cai , Ligang Liu

In the domain of 3D content creation, achieving optimal mesh topology through AI models has long been a pursuit for 3D artists. Previous methods, such as MeshGPT, have explored the generation of ready-to-use 3D objects via mesh…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Xianglong He , Junyi Chen , Di Huang , Zexiang Liu , Xiaoshui Huang , Wanli Ouyang , Chun Yuan , Yangguang Li

In this paper, we introduce Topology Sculptor, Shape Refiner (TSSR), a novel method for generating high-quality, artist-style 3D meshes based on Discrete Diffusion Models (DDMs). Our primary motivation for TSSR is to achieve highly accurate…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Kaiyu Song , Hanjiang Lai , Yaqing Zhang , Chuangjian Cai , Yan Pan Kun Yue , Jian Yin

Open-world 3D reconstruction models have recently garnered significant attention. However, without sufficient 3D inductive bias, existing methods typically entail expensive training costs and struggle to extract high-quality 3D meshes. In…

Computer Vision and Pattern Recognition · Computer Science 2024-08-20 Minghua Liu , Chong Zeng , Xinyue Wei , Ruoxi Shi , Linghao Chen , Chao Xu , Mengqi Zhang , Zhaoning Wang , Xiaoshuai Zhang , Isabella Liu , Hongzhi Wu , Hao Su

Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present DetailGen3D, a generative approach specifically designed to…

Computer Vision and Pattern Recognition · Computer Science 2025-04-02 Ken Deng , Yuan-Chen Guo , Jingxiang Sun , Zi-Xin Zou , Yangguang Li , Xin Cai , Yan-Pei Cao , Yebin Liu , Ding Liang

Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Zilong Chen , Yikai Wang , Feng Wang , Zhengyi Wang , Huaping Liu

Conventional production workflow of high-precision mesh assets necessitates a cumbersome and laborious process of manual sculpting by specialized 3D artists/modelers. The recent years have witnessed remarkable advances in AI-empowered 3D…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Qijian Zhang , Xiaozheng Jian , Xuan Zhang , Wenping Wang , Junhui Hou

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

Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Haoyu Wu , Meher Gitika Karumuri , Chuhang Zou , Seungbae Bang , Yuelong Li , Dimitris Samaras , Sunil Hadap

We introduce PartCrafter, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image. Unlike existing methods that either produce…

Computer Vision and Pattern Recognition · Computer Science 2025-06-09 Yuchen Lin , Chenguo Lin , Panwang Pan , Honglei Yan , Yiqiang Feng , Yadong Mu , Katerina Fragkiadaki

Recently, the surge of efficient and automated 3D AI-generated content (AIGC) methods has increasingly illuminated the path of transforming human imagination into complex 3D structures. However, the automated generation of 3D content is…

Graphics · Computer Science 2024-12-20 Pei Chen , Fudong Wang , Yixuan Tong , Jingdong Chen , Ming Yang , Minghui Yang

Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Daoyi Gao , Yawar Siddiqui , Lei Li , Angela Dai

We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality textures and geometries. Generally, two challenges remain in this field: i) existing methods struggle to generate…

Computer Vision and Pattern Recognition · Computer Science 2023-10-05 Xuanmeng Zhang , Jianfeng Zhang , Rohan Chacko , Hongyi Xu , Guoxian Song , Yi Yang , Jiashi Feng

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

Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models. Nevertheless, challenges persist in the form of multi-view geometric inconsistencies…

Computer Vision and Pattern Recognition · Computer Science 2023-11-20 Lincong Feng , Muyu Wang , Maoyu Wang , Kuo Xu , Xiaoli Liu

We present an end-to-end framework for generating artist-style meshes from noisy or incomplete point clouds, such as those captured by real-world sensors like LiDAR or mobile RGB-D cameras. Artist-created meshes are crucial for commercial…

Computer Vision and Pattern Recognition · Computer Science 2025-09-17 Yao He , Youngjoong Kwon , Wenxiao Cai , Ehsan Adeli

Deep generative models have shown success in generating 3D shapes with different representations. In this work, we propose Neural Volumetric Mesh Generator(NVMG) which can generate novel and high-quality volumetric meshes. Unlike the…

Computer Vision and Pattern Recognition · Computer Science 2022-10-10 Yan Zheng , Lemeng Wu , Xingchao Liu , Zhen Chen , Qiang Liu , Qixing Huang

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
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