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In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are…

Computer Vision and Pattern Recognition · Computer Science 2020-03-25 Yiyi Liao , Katja Schwarz , Lars Mescheder , Andreas Geiger

We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in…

Graphics · Computer Science 2025-06-02 Weiyu Li , Jiarui Liu , Hongyu Yan , Rui Chen , Yixun Liang , Xuelin Chen , Ping Tan , Xiaoxiao Long

Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by generating room meshes conditioned on textual style descriptions.…

Computer Vision and Pattern Recognition · Computer Science 2023-12-11 Jonas Schult , Sam Tsai , Lukas Höllein , Bichen Wu , Jialiang Wang , Chih-Yao Ma , Kunpeng Li , Xiaofang Wang , Felix Wimbauer , Zijian He , Peizhao Zhang , Bastian Leibe , Peter Vajda , Ji Hou

Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently. While existing methods support text-based generative texture generation or editing on 3D meshes, they often struggle to…

Computer Vision and Pattern Recognition · Computer Science 2024-03-05 Yudi Zhang , Qi Xu , Lei Zhang

A key challenge in fine-grained 3D-based interactive editing is the absence of an efficient representation that balances diverse modifications with high-quality view synthesis under a given memory constraint. While 3D meshes provide…

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

This paper presents a method to reconstruct high-quality textured 3D models from both multi-view and single-view images. The reconstruction is posed as an adaptation problem and is done progressively where in the first stage, we focus on…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Aysegul Dundar , Jun Gao , Andrew Tao , Bryan Catanzaro

In this work, we present HyperFlow - a novel generative model that leverages hypernetworks to create continuous 3D object representations in a form of lightweight surfaces (meshes), directly out of point clouds. Efficient object…

Computer Vision and Pattern Recognition · Computer Science 2020-06-17 Przemysław Spurek , Maciej Zięba , Jacek Tabor , Tomasz Trzciński

We propose a novel technique for adding geometric details to an input coarse 3D mesh guided by a text prompt. Our method is composed of three stages. First, we generate a single-view RGB image conditioned on the input coarse geometry and…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Yun-Chun Chen , Selena Ling , Zhiqin Chen , Vladimir G. Kim , Matheus Gadelha , Alec Jacobson

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

Recent studies on 3D hand reconstruction have demonstrated the effectiveness of synthetic training data to improve estimation performance. However, most methods rely on game engines to synthesize hand images, which often lack diversity in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Zhuoran Zhao , Xianghao Kong , Linlin Yang , Zheng Wei , Pan Hui , Anyi Rao

Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address this challenge,…

Machine Learning · Computer Science 2025-01-23 Jing Xiao , Xinhai Chen , Qingling Wang , Jie Liu

Learning radiance fields (NeRF) with powerful 2D diffusion models has garnered popularity for text-to-3D generation. Nevertheless, the implicit 3D representations of NeRF lack explicit modeling of meshes and textures over surfaces, and such…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Haibo Yang , Yang Chen , Yingwei Pan , Ting Yao , Zhineng Chen , Zuxuan Wu , Yu-Gang Jiang , Tao Mei

In this article, we provide a detailed survey of techniques for hexahedral mesh generation. We cover the whole spectrum of alternative approaches to mesh generation, as well as post processing algorithms for connectivity editing and mesh…

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

We present MeshLLM, a novel framework that leverages large language models (LLMs) to understand and generate text-serialized 3D meshes. Our approach addresses key limitations in existing methods, including the limited dataset scale when…

We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network. By projecting the output points onto this prior and simplifying the…

Computer Vision and Pattern Recognition · Computer Science 2020-10-21 Nitin Agarwal , M Gopi

4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, computationally expensive, and difficult to scale to longer sequences. We introduce a…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Dvir Samuel , Yuval Atzmon , Gal Chechik , Yoni Kasten

If a picture is worth thousand words, a moving 3d shape must be worth a million. We build upon the success of recent generative methods that create images fitting the semantics of a text prompt, and extend it to the controlled generation of…

Machine Learning · Computer Science 2021-09-28 Nikolay Jetchev

We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses. While many previous works learn to hallucinate the shape directly from priors, we resort to further improving the shape…

Computer Vision and Pattern Recognition · Computer Science 2019-08-19 Chao Wen , Yinda Zhang , Zhuwen Li , Yanwei Fu