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Model customization introduces new concepts to existing text-to-image models, enabling the generation of these new concepts/objects in novel contexts. However, such methods lack accurate camera view control with respect to the new object,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-04 Nupur Kumari , Grace Su , Richard Zhang , Taesung Park , Eli Shechtman , Jun-Yan Zhu

Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, detailed edit and customization of 3D assets remains a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-09 Zhangyang Qi , Yunhan Yang , Mengchen Zhang , Long Xing , Xiaoyang Wu , Tong Wu , Dahua Lin , Xihui Liu , Jiaqi Wang , Hengshuang Zhao

Precise geometric control in image generation is essential for engineering \& product design and creative industries to control 3D object features accurately in image space. Traditional 3D editing approaches are time-consuming and demand…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Phillip Mueller , Talip Uenlue , Sebastian Schmidt , Marcel Kollovieh , Jiajie Fan , Stephan Guennemann , Lars Mikelsons

The creation of high-quality 3D assets is paramount for applications in digital heritage preservation, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for the modeling,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Shen Chen , Jiale Zhou , Zhongyu Jiang , Tianfang Zhang , Zongkai Wu , Jenq-Neng Hwang , Lei 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

In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including…

Computer Vision and Pattern Recognition · Computer Science 2023-03-23 Yen-Chi Cheng , Hsin-Ying Lee , Sergey Tulyakov , Alexander Schwing , Liangyan Gui

Customized video generation aims to generate high-quality videos guided by text prompts and subject's reference images. However, since it is only trained on static images, the fine-tuning process of subject learning disrupts abilities of…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Tao Wu , Yong Zhang , Xintao Wang , Xianpan Zhou , Guangcong Zheng , Zhongang Qi , Ying Shan , Xi Li

Recent years have witnessed remarkable progress in multi-view diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Zeyi Sun , Tong Wu , Pan Zhang , Yuhang Zang , Xiaoyi Dong , Yuanjun Xiong , Dahua Lin , Jiaqi Wang

Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization with consistent identity and interaction. In this paper, we propose PolyVivid, a multi-subject…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Teng Hu , Zhentao Yu , Zhengguang Zhou , Jiangning Zhang , Yuan Zhou , Qinglin Lu , Ran Yi

Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantial resource demands for training, along with inefficient,…

Computer Vision and Pattern Recognition · Computer Science 2024-09-11 Ka-Hei Hui , Aditya Sanghi , Arianna Rampini , Kamal Rahimi Malekshan , Zhengzhe Liu , Hooman Shayani , Chi-Wing Fu

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

Realistic and diverse 3D shape generation is helpful for a wide variety of applications such as virtual reality, gaming, and animation. Modern generative models, such as GANs and diffusion models, learn from large-scale datasets and…

Computer Vision and Pattern Recognition · Computer Science 2023-05-22 Jingyuan Zhu , Huimin Ma , Jiansheng Chen , Jian Yuan

We propose a training-free approach to 3D editing that enables the editing of a single shape within a few minutes. The edited 3D mesh aligns well with the prompts, and remains identical for regions that are not intended to be altered. To…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Ziya Erkoç , Can Gümeli , Chaoyang Wang , Matthias Nießner , Angela Dai , Peter Wonka , Hsin-Ying Lee , Peiye Zhuang

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

With the advance of diffusion models, various personalized image generation methods have been proposed. However, almost all existing work only focuses on either subject-driven or style-driven personalization. Meanwhile, state-of-the-art…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Youcan Xu , Zhen Wang , Jun Xiao , Wei Liu , Long Chen

Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3D data. Due to the open-ended nature of the task, most…

Computer Vision and Pattern Recognition · Computer Science 2024-04-18 Yuze He , Yushi Bai , Matthieu Lin , Wang Zhao , Yubin Hu , Jenny Sheng , Ran Yi , Juanzi Li , Yong-Jin Liu

There is a growing demand for customized and expressive 3D characters with the emergence of AI agents and Metaverse, but creating 3D characters using traditional computer graphics tools is a complex and time-consuming task. To address these…

Computer Vision and Pattern Recognition · Computer Science 2023-12-27 Jianqiang Ren , Chao He , Lin Liu , Jiahao Chen , Yutong Wang , Yafei Song , Jianfang Li , Tangli Xue , Siqi Hu , Tao Chen , Kunkun Zheng , Jianjing Xiang , Liefeng Bo

This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques or recent approaches that employ multi-stage…

Computer Vision and Pattern Recognition · Computer Science 2025-07-18 Zehuan Huang , Yuan-Chen Guo , Xingqiao An , Yunhan Yang , Yangguang Li , Zi-Xin Zou , Ding Liang , Xihui Liu , Yan-Pei Cao , Lu Sheng

In recent times, the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency, but their…

Computer Vision and Pattern Recognition · Computer Science 2024-05-14 Taoran Yi , Jiemin Fang , Junjie Wang , Guanjun Wu , Lingxi Xie , Xiaopeng Zhang , Wenyu Liu , Qi Tian , Xinggang Wang

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
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