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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

Recent conditional image generation methods can improve controllability by generating images that are faithful to conditions such as sketches, human poses, segmentation maps, and depth. By applying these techniques to image augmentation…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Shogo Noguchi

3D world generation is essential for applications such as immersive content creation or autonomous driving simulation. Recent advances in 3D world generation have shown promising results; however, these methods are constrained by grid…

Computer Vision and Pattern Recognition · Computer Science 2026-05-04 Jaeyoung Chung , Suyoung Lee , Jianfeng Xiang , Jiaolong Yang , Kyoung Mu Lee

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

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

Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As a result, the generated scenes are often object-centric and…

Computer Vision and Pattern Recognition · Computer Science 2024-11-22 Heng Yu , Chaoyang Wang , Peiye Zhuang , Willi Menapace , Aliaksandr Siarohin , Junli Cao , Laszlo A Jeni , Sergey Tulyakov , Hsin-Ying Lee

We are witnessing significant breakthroughs in the technology for generating 3D objects from text. Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric…

Computer Vision and Pattern Recognition · Computer Science 2023-12-15 Qihang Zhang , Chaoyang Wang , Aliaksandr Siarohin , Peiye Zhuang , Yinghao Xu , Ceyuan Yang , Dahua Lin , Bolei Zhou , Sergey Tulyakov , Hsin-Ying Lee

In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are…

Computer Vision and Pattern Recognition · Computer Science 2020-03-25 Yiyi Liao , Katja Schwarz , Lars Mescheder , Andreas Geiger

As Artificial Intelligence Generated Content (AIGC) advances, a variety of methods have been developed to generate text, images, videos, and 3D objects from single or multimodal inputs, contributing efforts to emulate human-like cognitive…

Computer Vision and Pattern Recognition · Computer Science 2024-08-21 Yiying Yang , Fukun Yin , Jiayuan Fan , Xin Chen , Wanzhang Li , Gang Yu

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

Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Zhifei Yang , Keyang Lu , Chao Zhang , Jiaxing Qi , Hanqi Jiang , Ruifei Ma , Shenglin Yin , Yifan Xu , Mingzhe Xing , Zhen Xiao , Jieyi Long , Guangyao Zhai

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

We present SceneFactor, a diffusion-based approach for large-scale 3D scene generation that enables controllable generation and effortless editing. SceneFactor enables text-guided 3D scene synthesis through our factored diffusion…

Computer Vision and Pattern Recognition · Computer Science 2024-12-04 Alexey Bokhovkin , Quan Meng , Shubham Tulsiani , Angela Dai

High-level autonomous operations depend on a robot's ability to construct a sufficiently expressive model of its environment. Traditional three-dimensional (3D) scene representations, such as point clouds and occupancy grids, provide…

Robotics · Computer Science 2025-06-10 Chad R Samuelson , Timothy W McLain , Joshua G Mangelson

3D-consistent image generation from a single 2D semantic label is an important and challenging research topic in computer graphics and computer vision. Although some related works have made great progress in this field, most of the existing…

Computer Vision and Pattern Recognition · Computer Science 2024-03-12 Bo Li , Yi-ke Li , Zhi-fen He , Bin Liu , Yun-Kun Lai

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical sets. To address this, we introduce the task of cinematic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Kaiyi Huang , Yukun Huang , Yu Li , Jianhong Bai , Xintao Wang , Zinan Lin , Xuefei Ning , Jiwen Yu , Pengfei Wan , Yu Wang , Xihui Liu

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

City scene generation has gained significant attention in autonomous driving, smart city development, and traffic simulation. It helps enhance infrastructure planning and monitoring solutions. Existing methods have employed a two-stage…

Computer Vision and Pattern Recognition · Computer Science 2024-06-10 Jie Deng , Wenhao Chai , Junsheng Huang , Zhonghan Zhao , Qixuan Huang , Mingyan Gao , Jianshu Guo , Shengyu Hao , Wenhao Hu , Jenq-Neng Hwang , Xi Li , Gaoang Wang

Automatically generating high-quality real world 3D scenes is of enormous interest for applications such as virtual reality and robotics simulation. Towards this goal, we introduce NeuralField-LDM, a generative model capable of synthesizing…

Computer Vision and Pattern Recognition · Computer Science 2023-04-20 Seung Wook Kim , Bradley Brown , Kangxue Yin , Karsten Kreis , Katja Schwarz , Daiqing Li , Robin Rombach , Antonio Torralba , Sanja Fidler