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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

Visual spatial description (VSD) aims to generate texts that describe the spatial relations of the given objects within images. Existing VSD work merely models the 2D geometrical vision features, thus inevitably falling prey to the problem…

Computer Vision and Pattern Recognition · Computer Science 2023-05-26 Yu Zhao , Hao Fei , Wei Ji , Jianguo Wei , Meishan Zhang , Min Zhang , Tat-Seng Chua

Synthesizing consistent and photorealistic 3D scenes is an open problem in computer vision. Video diffusion models generate impressive videos but cannot directly synthesize 3D representations, i.e., lack 3D consistency in the generated…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Katja Schwarz , Norman Mueller , Peter Kontschieder

Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of domains including spatial intelligence, embodied intelligence, and autonomous driving. While…

Computer Vision and Pattern Recognition · Computer Science 2026-05-14 Hanxin Zhu , Cong Wang , Peiyan Tu , Jiayi Luo , Tianyu He , Xin Jin , Zhibo Chen

We propose a novel image editing technique that enables 3D manipulations on single images, such as object rotation and translation. Existing 3D-aware image editing approaches typically rely on synthetic multi-view datasets for training…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Ruicheng Wang , Jianfeng Xiang , Jiaolong Yang , Xin Tong

The rapid development of Large Multimodal Models (LMMs) has led to remarkable progress in 2D visual understanding; however, extending these capabilities to 3D scene understanding remains a significant challenge. Existing approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-09-05 Hongpei Zheng , Lintao Xiang , Qijun Yang , Qian Lin , Hujun Yin

Recent advances in diffusion models have significantly improved 3D generation, enabling the use of assets generated from an image for embodied AI simulations. However, the one-to-many nature of the image-to-3D problem limits their use due…

Computer Vision and Pattern Recognition · Computer Science 2025-02-26 Onat Şahin , Mohammad Altillawi , George Eskandar , Carlos Carbone , Ziyuan Liu

3D Gaussian Splatting (3DGS) has emerged as a leading approach for high-quality novel view synthesis, with numerous variants extending its applicability to a broad spectrum of 3D and 4D scene reconstruction tasks. Despite its success, the…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Yiming Wang , Shaofei Wang , Marko Mihajlovic , Siyu Tang

Text-to-3D with diffusion models has achieved remarkable progress in recent years. However, existing methods either rely on score distillation-based optimization which suffer from slow inference, low diversity and Janus problems, or are…

Computer Vision and Pattern Recognition · Computer Science 2023-11-27 Jiahao Li , Hao Tan , Kai Zhang , Zexiang Xu , Fujun Luan , Yinghao Xu , Yicong Hong , Kalyan Sunkavalli , Greg Shakhnarovich , Sai Bi

Recent progress in text-to-3D object generation enables the synthesis of detailed geometry from text input by leveraging 2D diffusion models and differentiable 3D representations. However, the approaches often suffer from limited…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Ming He , Zhixiang Chen , Steve Maddock

We introduce a high resolution, 3D-consistent image and shape generation technique which we call StyleSDF. Our method is trained on single-view RGB data only, and stands on the shoulders of StyleGAN2 for image generation, while solving two…

Computer Vision and Pattern Recognition · Computer Science 2022-03-31 Roy Or-El , Xuan Luo , Mengyi Shan , Eli Shechtman , Jeong Joon Park , Ira Kemelmacher-Shlizerman

While text-to-3D and image-to-3D generation tasks have received considerable attention, one important but under-explored field between them is controllable text-to-3D generation, which we mainly focus on in this work. To address this task,…

Computer Vision and Pattern Recognition · Computer Science 2025-02-11 Zhiqi Li , Yiming Chen , Lingzhe Zhao , Peidong Liu

Generative methods for 3D assets have recently achieved remarkable progress, yet providing intuitive and precise control over the object geometry remains a key challenge. Existing approaches predominantly rely on text or image prompts,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Elisabetta Fedele , Francis Engelmann , Ian Huang , Or Litany , Marc Pollefeys , Leonidas Guibas

We propose a method for self-supervised image representation learning under the guidance of 3D geometric consistency. Our intuition is that 3D geometric consistency priors such as smooth regions and surface discontinuities may imply…

Computer Vision and Pattern Recognition · Computer Science 2022-03-30 Nenglun Chen , Lei Chu , Hao Pan , Yan Lu , Wenping Wang

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

High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a…

Computer Vision and Pattern Recognition · Computer Science 2025-09-08 Arianna Rampini , Kanika Madan , Bruno Roy , AmirHossein Zamani , Derek Cheung

The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant…

Computer Vision and Pattern Recognition · Computer Science 2024-11-21 Luoxi Zhang , Pragyan Shrestha , Yu Zhou , Chun Xie , Itaru Kitahara

Generating high-quality 3D assets from text and images has long been challenging, primarily due to the absence of scalable 3D representations capable of capturing intricate geometry distributions. In this work, we introduce Direct3D, a…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 Shuang Wu , Youtian Lin , Feihu Zhang , Yifei Zeng , Jingxi Xu , Philip Torr , Xun Cao , Yao Yao

Novel view synthesis has shown rapid progress recently, with methods capable of producing increasingly photorealistic results. 3D Gaussian Splatting has emerged as a promising method, producing high-quality renderings of scenes and enabling…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Richard Shaw , Michal Nazarczuk , Jifei Song , Arthur Moreau , Sibi Catley-Chandar , Helisa Dhamo , Eduardo Perez-Pellitero

Score Distillation Sampling (SDS) by well-trained 2D diffusion models has shown great promise in text-to-3D generation. However, this paradigm distills view-agnostic 2D image distributions into the rendering distribution of 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Chenhan Jiang , Yihan Zeng , Tianyang Hu , Songcun Xu , Wei Zhang , Hang Xu , Dit-Yan Yeung
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