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Related papers: SceneGen: Single-Image 3D Scene Generation in One …

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Synthesizing interactive 3D scenes from text is essential for gaming, virtual reality, and embodied AI. However, existing methods face several challenges. Learning-based approaches depend on small-scale indoor datasets, limiting the scene…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Lu Ling , Chen-Hsuan Lin , Tsung-Yi Lin , Yifan Ding , Yu Zeng , Yichen Sheng , Yunhao Ge , Ming-Yu Liu , Aniket Bera , Zhaoshuo Li

We present a novel diffusion-based approach for coherent 3D scene reconstruction from a single RGB image. Our method utilizes an image-conditioned 3D scene diffusion model to simultaneously denoise the 3D poses and geometries of all objects…

Computer Vision and Pattern Recognition · Computer Science 2024-12-16 Manuel Dahnert , Angela Dai , Norman Müller , Matthias Nießner

The generation of high-quality 3D environments is crucial for industries such as gaming, virtual reality, and cinema, yet remains resource-intensive due to the reliance on manual processes. This study performs a systematic review of…

Graphics · Computer Science 2025-06-09 Miguel Silva , Alexandre Valle de Carvalho

We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such…

Computer Vision and Pattern Recognition · Computer Science 2023-05-31 Rafail Fridman , Amit Abecasis , Yoni Kasten , Tali Dekel

We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given a room with a specified size, our method automatically…

Computer Vision and Pattern Recognition · Computer Science 2021-08-23 Ming-Jia Yang , Yu-Xiao Guo , Bin Zhou , Xin Tong

Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects. Here we introduce Lay-A-Scene, which solves the task of…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Ohad Rahamim , Hilit Segev , Idan Achituve , Yuval Atzmon , Yoni Kasten , Gal Chechik

Generative world models for autonomous driving (AD) have become a trending topic. Unlike the widely studied image modality, in this work we explore generative world models for LiDAR data. Existing generation methods for LiDAR data only…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Sizhuo Zhou , Xiaosong Jia , Fanrui Zhang , Junjie Li , Juyong Zhang , Yukang Feng , Jianwen Sun , Songbur Wong , Junqi You , Junchi Yan

In this paper, we study video synthesis with emphasis on simplifying the generation conditions. Most existing video synthesis models or datasets are designed to address complex motions of a single object, lacking the ability of…

Computer Vision and Pattern Recognition · Computer Science 2023-05-24 Yang Wu , Zhibin Liu , Hefeng Wu , Liang Lin

Designing high-quality indoor 3D scenes is important in many practical applications, such as room planning or game development. Conventionally, this has been a time-consuming process which requires both artistic skill and familiarity with…

Computer Vision and Pattern Recognition · Computer Science 2024-07-31 Başak Melis Öcal , Maxim Tatarchenko , Sezer Karaoglu , Theo Gevers

Simulation is crucial for developing and evaluating autonomous vehicle (AV) systems. Recent literature builds on a new generation of generative models to synthesize highly realistic images for full-stack simulation. However, purely…

Computer Vision and Pattern Recognition · Computer Science 2025-06-25 Zehao Zhu , Yuliang Zou , Chiyu Max Jiang , Bo Sun , Vincent Casser , Xiukun Huang , Jiahao Wang , Zhenpei Yang , Ruiqi Gao , Leonidas Guibas , Mingxing Tan , Dragomir Anguelov

We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true…

Computer Vision and Pattern Recognition · Computer Science 2021-01-19 Shuhan Tan , Kelvin Wong , Shenlong Wang , Sivabalan Manivasagam , Mengye Ren , Raquel Urtasun

The field of generative AI has a transformative impact on various areas, including virtual reality, autonomous driving, the metaverse, gaming, and robotics. Among these applications, 3D object generation techniques are of utmost importance.…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Qinghong Sun , Yangguang Li , ZeXiang Liu , Xiaoshui Huang , Fenggang Liu , Xihui Liu , Wanli Ouyang , Jing Shao

Generating a street-level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade-off: geometry-colorization models achieve high geometric fidelity but are typically building-focused…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Ming Qian , Zimin Xia , Changkun Liu , Shuailei Ma , Wen Wang , Zeran Ke , Bin Tan , Hang Zhang , Gui-Song Xia

This paper presents a novel approach to inpainting 3D regions of a scene, given masked multi-view images, by distilling a 2D diffusion model into a learned 3D scene representation (e.g. a NeRF). Unlike 3D generative methods that explicitly…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Kira Prabhu , Jane Wu , Lynn Tsai , Peter Hedman , Dan B Goldman , Ben Poole , Michael Broxton

In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling…

Graphics · Computer Science 2025-09-23 Geonung Kim , Janghyeok Han , Sunghyun Cho

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

We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared to works that bake shading in the 3D object's appearance,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-03 Yawar Siddiqui , Tom Monnier , Filippos Kokkinos , Mahendra Kariya , Yanir Kleiman , Emilien Garreau , Oran Gafni , Natalia Neverova , Andrea Vedaldi , Roman Shapovalov , David Novotny

We propose UniSeg3D, a unified 3D scene understanding framework that achieves panoptic, semantic, instance, interactive, referring, and open-vocabulary segmentation tasks within a single model. Most previous 3D segmentation approaches are…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Wei Xu , Chunsheng Shi , Sifan Tu , Xin Zhou , Dingkang Liang , Xiang Bai

Tremendous progress in visual scene generation now turns a single image into an explorable 3D world, yet immersion remains incomplete without sound. We introduce Image2AVScene, the task of generating a 3D audio-visual scene from a single…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Derong Jin , Xiyi Chen , Ming C. Lin , Ruohan Gao

When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. When entering space that was previously obstructed from view such as turning corners in hallways or…

Robotics · Computer Science 2024-03-19 Alec Reed , Brendan Crowe , Doncey Albin , Lorin Achey , Bradley Hayes , Christoffer Heckman