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Related papers: One2Scene: Geometric Consistent Explorable 3D Scen…

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We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Jing Yu Koh , Harsh Agrawal , Dhruv Batra , Richard Tucker , Austin Waters , Honglak Lee , Yinfei Yang , Jason Baldridge , Peter Anderson

Recent progress in image and video synthesis has inspired their use in advancing 3D scene generation. However, we observe that text-to-image and -video approaches struggle to maintain scene- and object-level consistency beyond a limited…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Manuel-Andreas Schneider , Angela Dai

The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and world modeling. While panoramic images have proven effective…

Computer Vision and Pattern Recognition · Computer Science 2026-05-04 Felix Wimbauer , Fabian Manhardt , Michael Oechsle , Nikolai Kalischek , Christian Rupprecht , Daniel Cremers , Federico Tombari

Creating flexible 3D scenes from a single image is vital when direct 3D data acquisition is costly or impractical. We introduce NavCrafter, a novel framework that explores 3D scenes from a single image by synthesizing novel-view video…

Computer Vision and Pattern Recognition · Computer Science 2026-04-06 Hongbo Duan , Peiyu Zhuang , Yi Liu , Zhengyang Zhang , Yuxin Zhang , Pengting Luo , Fangming Liu , Xueqian Wang

Generative reconstruction methods compute the 3D configuration (such as pose and/or geometry) of a shape by optimizing the overlap of the projected 3D shape model with images. Proper handling of occlusions is a big challenge, since the…

Computer Vision and Pattern Recognition · Computer Science 2016-02-12 Helge Rhodin , Nadia Robertini , Christian Richardt , Hans-Peter Seidel , Christian Theobalt

Reconstructing complete and interactive 3D scenes remains a fundamental challenge in computer vision and robotics, particularly due to persistent object occlusions and limited sensor coverage. Multiview observations from a single scene scan…

Computer Vision and Pattern Recognition · Computer Science 2025-08-19 Wenhao Hu , Zesheng Li , Haonan Zhou , Liu Liu , Xuexiang Wen , Zhizhong Su , Xi Li , Gaoang Wang

Single-view 3D reconstruction is currently approached from two dominant perspectives: reconstruction of scenes with limited diversity using 3D data supervision or reconstruction of diverse singular objects using large image priors. However,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Andreea Ardelean , Mert Özer , Bernhard Egger

Reconstructing physically stable 3D scenes from a single RGB image enables casual images to be converted into simulation-ready digital assets for applications such as immersive interaction and content creation. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Xiaoxuan Ma , Jiashun Wang , Nicolas Ugrinovic , Yehonathan Litman , Kris Kitani

The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360$^{\circ}$ scene generation pipeline that facilitates the creation of comprehensive…

Computer Vision and Pattern Recognition · Computer Science 2024-07-26 Shijie Zhou , Zhiwen Fan , Dejia Xu , Haoran Chang , Pradyumna Chari , Tejas Bharadwaj , Suya You , Zhangyang Wang , Achuta Kadambi

We present GuidedSceneGen, a text-to-3D generation framework that produces metrically accurate, globally consistent, and semantically interpretable indoor scenes. Unlike prior text-driven methods that often suffer from geometric drift or…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Stefan Ainetter , Thomas Deixelberger , Edoardo A. Dominici , Philipp Drescher , Konstantinos Vardis , Markus Steinberger

3D scene generation is a core technology for gaming, film/VFX, and VR/AR. Growing demand for rapid iteration, high-fidelity detail, and accessible content creation has further increased interest in this area. Existing methods broadly follow…

Computer Vision and Pattern Recognition · Computer Science 2026-01-29 Haozhi Zhu , Miaomiao Zhao , Dingyao Liu , Runze Tian , Yan Zhang , Jie Guo , Fenggen Yu

3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames…

Computer Vision and Pattern Recognition · Computer Science 2024-05-01 Paul Engstler , Andrea Vedaldi , Iro Laina , Christian Rupprecht

Controllable scene synthesis aims to create interactive environments for various industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a compact manner.…

Computer Vision and Pattern Recognition · Computer Science 2024-01-02 Guangyao Zhai , Evin Pınar Örnek , Shun-Cheng Wu , Yan Di , Federico Tombari , Nassir Navab , Benjamin Busam

Text-driven 3D scene generation has seen significant advancements recently. However, most existing methods generate single-view images using generative models and then stitch them together in 3D space. This independent generation for each…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Wenrui Li , Fucheng Cai , Yapeng Mi , Zhe Yang , Wangmeng Zuo , Xingtao Wang , Xiaopeng Fan

Scene-level 3D generation represents a critical frontier in multimedia and computer graphics, yet existing approaches either suffer from limited object categories or lack editing flexibility for interactive applications. In this paper, we…

Graphics · Computer Science 2025-04-18 Wenqi Dong , Bangbang Yang , Zesong Yang , Yuan Li , Tao Hu , Hujun Bao , Yuewen Ma , Zhaopeng Cui

Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, due to the lack of reliable geometric supervision, they…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Junfeng Ni , Yixin Chen , Zhifei Yang , Yu Liu , Ruijie Lu , Song-Chun Zhu , Siyuan Huang

How can one efficiently generate high-quality, wide-scope 3D scenes from arbitrary single images? Existing methods suffer several drawbacks, such as requiring multi-view data, time-consuming per-scene optimization, distorted geometry in…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Hanwen Liang , Junli Cao , Vidit Goel , Guocheng Qian , Sergei Korolev , Demetri Terzopoulos , Konstantinos N. Plataniotis , Sergey Tulyakov , Jian Ren

Explorable 3D world generation from a single image or text prompt forms a cornerstone of spatial intelligence. Recent works utilize video model to achieve wide-scope and generalizable 3D world generation. However, existing approaches often…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Zhongqi Yang , Wenhang Ge , Yuqi Li , Jiaqi Chen , Haoyuan Li , Mengyin An , Fei Kang , Hua Xue , Baixin Xu , Yuyang Yin , Eric Li , Yang Liu , Yikai Wang , Hao-Xiang Guo , Yahui Zhou

Text-driven 3D indoor scene generation holds broad applications, ranging from gaming and smart homes to AR/VR applications. Fast and high-fidelity scene generation is paramount for ensuring user-friendly experiences. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2024-05-10 Yikun Ma , Dandan Zhan , Zhi Jin

We present WonderWorld, a novel framework for interactive 3D scene generation that enables users to interactively specify scene contents and layout and see the created scenes in low latency. The major challenge lies in achieving fast…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Hong-Xing Yu , Haoyi Duan , Charles Herrmann , William T. Freeman , Jiajun Wu