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The demand for efficient 3D model generation techniques has grown exponentially, as manual creation of 3D models is time-consuming and requires specialized expertise. While generative models have shown potential in creating 3D textured…

Computer Vision and Pattern Recognition · Computer Science 2023-07-28 Fanghua Yu , Xintao Wang , Zheyuan Li , Yan-Pei Cao , Ying Shan , Chao Dong

In this paper, we address the critical bottleneck in robotics caused by the scarcity of diverse 3D data by presenting a novel two-stage approach for generating high-quality 3D models from a single image. This method is motivated by the need…

Computer Vision and Pattern Recognition · Computer Science 2025-02-20 Nan Huang , Ting Zhang , Yuhui Yuan , Dong Chen , Shanghang Zhang

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

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture…

Computer Vision and Pattern Recognition · Computer Science 2023-10-27 Jingxiang Sun , Bo Zhang , Ruizhi Shao , Lizhen Wang , Wen Liu , Zhenda Xie , Yebin Liu

Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Haibo Yang , Yang Chen , Yingwei Pan , Ting Yao , Zhineng Chen , Chong-Wah Ngo , Tao Mei

We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but are limited in spatial extent and quality when extended to…

Computer Vision and Pattern Recognition · Computer Science 2025-05-02 Quan Meng , Lei Li , Matthias Nießner , Angela Dai

Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation. Extending DDMs for 3D content creation is an active field of research. Here, we propose TetraDiffusion, a diffusion model that operates on a…

Computer Vision and Pattern Recognition · Computer Science 2024-08-12 Nikolai Kalischek , Torben Peters , Jan D. Wegner , Konrad Schindler

Recently, multi-view diffusion-based 3D generation methods have gained significant attention. However, these methods often suffer from shape and texture misalignment across generated multi-view images, leading to low-quality 3D generation…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Zhuojiang Cai , Yiheng Zhang , Meitong Guo , Mingdao Wang , Yuwang Wang

Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view synthesis as a…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Ruishu Zhu , Zhihao Huang , Jiacheng Sun , Ping Luo , Hongyuan Zhang , Xuelong Li

3D content creation from a single image is a long-standing yet highly desirable task. Recent advances introduce 2D diffusion priors, yielding reasonable results. However, existing methods are not hyper-realistic enough for post-generation…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Tong Wu , Zhibing Li , Shuai Yang , Pan Zhang , Xinggang Pan , Jiaqi Wang , Dahua Lin , Ziwei Liu

We propose a unified framework aimed at enhancing the diffusion priors for 3D generation tasks. Despite the critical importance of these tasks, existing methodologies often struggle to generate high-caliber results. We begin by examining…

Computer Vision and Pattern Recognition · Computer Science 2023-12-11 Xiaofeng Yang , Yiwen Chen , Cheng Chen , Chi Zhang , Yi Xu , Xulei Yang , Fayao Liu , Guosheng Lin

This paper introduces KeyDiff3D, a framework for unsupervised monocular 3D keypoints estimation that accurately predicts 3D keypoints from a single image. While previous methods rely on manual annotations or calibrated multi-view images,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-17 Subin Jeon , In Cho , Junyoung Hong , Seon Joo Kim

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

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Cheng Chen , Xiaofeng Yang , Fan Yang , Chengzeng Feng , Zhoujie Fu , Chuan-Sheng Foo , Guosheng Lin , Fayao Liu

Creating realistic 3D objects and clothed avatars from a single RGB image is an attractive yet challenging problem. Due to its ill-posed nature, recent works leverage powerful prior from 2D diffusion models pretrained on large datasets.…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Yuxuan Xue , Xianghui Xie , Riccardo Marin , Gerard Pons-Moll

Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content.However,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Zhenyu Tang , Junwu Zhang , Xinhua Cheng , Wangbo Yu , Chaoran Feng , Yatian Pang , Bin Lin , Li Yuan

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Zeren Xiong , Zikun Chen , Zedong Zhang , Xiang Li , Ying Tai , Jian Yang , Jun Li

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

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