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In this paper, we propose an effective two-stage approach named Grounded-Dreamer to generate 3D assets that can accurately follow complex, compositional text prompts while achieving high fidelity by using a pre-trained multi-view diffusion…

Computer Vision and Pattern Recognition · Computer Science 2024-04-30 Xiaolong Li , Jiawei Mo , Ying Wang , Chethan Parameshwara , Xiaohan Fei , Ashwin Swaminathan , CJ Taylor , Zhuowen Tu , Paolo Favaro , Stefano Soatto

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

Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets. However, generating 3D compositional objects from single images--particularly under occlusions--remains challenging. Existing methods often…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Hui Shan , Keyang Luo , Ming Li , Sizhe Zheng , Yanwei Fu , Zhen Chen , Xiangru Huang

Recently, the field of text-guided 3D scene generation has garnered significant attention. High-quality generation that aligns with physical realism and high controllability is crucial for practical 3D scene applications. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2026-03-11 Yang Zhou , Zongjin He , Qixuan Li , Chao Wang

Text-to-3D form plays a crucial role in creating editable 3D scenes for AR/VR. Recent advances have shown promise in merging neural radiance fields (NeRFs) with pre-trained diffusion models for text-to-3D object generation. However, one…

Computer Vision and Pattern Recognition · Computer Science 2024-09-25 Haotian Bai , Yuanhuiyi Lyu , Lutao Jiang , Sijia Li , Haonan Lu , Xiaodong Lin , Lin Wang

Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Bohan Zeng , Shanglin Li , Yutang Feng , Ling Yang , Hong Li , Sicheng Gao , Jiaming Liu , Conghui He , Wentao Zhang , Jianzhuang Liu , Baochang Zhang , Shuicheng Yan

As pretrained text-to-image diffusion models become increasingly powerful, recent efforts have been made to distill knowledge from these text-to-image pretrained models for optimizing a text-guided 3D model. Most of the existing methods…

Computer Vision and Pattern Recognition · Computer Science 2024-06-12 Gege Gao , Weiyang Liu , Anpei Chen , Andreas Geiger , Bernhard Schölkopf

This paper presents a novel decoder-based approach for generating manufacturable 3D structures optimized for additive manufacturing. We introduce a deep learning framework that decodes latent representations into geometrically valid,…

Computer Vision and Pattern Recognition · Computer Science 2026-01-14 Abhishek Kumar

Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning. However, most of the existing approaches rely on auto-encoding…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Whie Jung , Jaehoon Yoo , Sungjin Ahn , Seunghoon Hong

We introduce SceneTransporter, an end-to-end framework for structured 3D scene generation from a single image. While existing methods generate part-level 3D objects, they often fail to organize these parts into distinct instances in…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Ling Wang , Hao-Xiang Guo , Xinzhou Wang , Fuchun Sun , Kai Sun , Pengkun Liu , Hang Xiao , Zhong Wang , Guangyuan Fu , Eric Li , Yang Liu , Yikai Wang

Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, generating multiple objects with reasonable interactions within a…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Chongjian Ge , Chenfeng Xu , Yuanfeng Ji , Chensheng Peng , Masayoshi Tomizuka , Ping Luo , Mingyu Ding , Varun Jampani , Wei Zhan

3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Haozhe Xie , Zhaoxi Chen , Fangzhou Hong , Ziwei Liu

Compositional scene reconstruction seeks to create object-centric representations rather than holistic scenes from real-world videos, which is natively applicable for simulation and interaction. Conventional compositional reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Chong Xia , Kai Zhu , Zizhuo Wang , Fangfu Liu , Zhizheng Zhang , Yueqi Duan

3D city generation is a desirable yet challenging task, since humans are more sensitive to structural distortions in urban environments. Additionally, generating 3D cities is more complex than 3D natural scenes since buildings, as objects…

Computer Vision and Pattern Recognition · Computer Science 2024-06-07 Haozhe Xie , Zhaoxi Chen , Fangzhou Hong , Ziwei Liu

Existing text-to-3D and image-to-3D models often struggle with complex scenes involving multiple objects and intricate interactions. Although some recent attempts have explored such compositional scenarios, they still require an extensive…

Computer Vision and Pattern Recognition · Computer Science 2025-04-09 Yujia Hu , Songhua Liu , Xingyi Yang , Xinchao Wang

Text-to-Image (T2I) generation has long been an open problem, with compositional synthesis remaining particularly challenging. This task requires accurate rendering of complex scenes containing multiple objects that exhibit diverse…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Shijian Wang , Runhao Fu , Siyi Zhao , Qingqin Zhan , Xingjian Wang , Jiarui Jin , Yuan Lu , Hanqian Wu , Cunjian Chen

Generating realistic 3D indoor scenes from user inputs remains a challenging problem in computer vision and graphics, requiring careful balance of geometric consistency, spatial relationships, and visual realism. While neural generation…

Computer Vision and Pattern Recognition · Computer Science 2025-06-30 Mengqi Zhou , Xipeng Wang , Yuxi Wang , Zhaoxiang Zhang

Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content. However, the scarcity of 3D scene datasets constrains current methodologies to primarily object-centric…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Dejia Xu , Hanwen Liang , Neel P. Bhatt , Hezhen Hu , Hanxue Liang , Konstantinos N. Plataniotis , Zhangyang Wang

Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Tobias Sautter , Jan-Niklas Dihlmann , Hendrik P. A. Lensch

We present Layout-Your-3D, a framework that allows controllable and compositional 3D generation from text prompts. Existing text-to-3D methods often struggle to generate assets with plausible object interactions or require tedious…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Junwei Zhou , Xueting Li , Lu Qi , Ming-Hsuan Yang
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