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Text-to-3D scene generation from natural language is highly desirable for digital content creation. However, existing methods are largely domain-restricted or reliant on predefined spatial relationships, limiting their capacity for…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Jun Luo , Jiaxiang Tang , Ruijie Lu , Gang Zeng

3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Yongzhi Xu , Yonhon Ng , Yifu Wang , Inkyu Sa , Yunfei Duan , Zhenhong Sun , Yang Li , Pan Ji , Hongdong Li

3D scene understanding has gained significant attention due to its wide range of applications. However, existing methods for 3D scene understanding are limited to specific downstream tasks, which hinders their practicality in real-world…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Zehan Wang , Haifeng Huang , Yang Zhao , Ziang Zhang , Zhou Zhao

Generative AI (GenAI) has significantly advanced the ease and flexibility of image creation. However, it remains a challenge to precisely control spatial compositions, including object arrangement and scene conditions. To bridge this gap,…

Human-Computer Interaction · Computer Science 2025-08-12 Runlin Duan , Yuzhao Chen , Rahul Jain , Yichen Hu , Jingyu Shi , Karthik Ramani

Novel view synthesis for underwater scene reconstruction presents unique challenges due to complex light-media interactions. Optical scattering and absorption in water body bring inhomogeneous medium attenuation interference that disrupts…

Computer Vision and Pattern Recognition · Computer Science 2026-05-14 Jieyu Yuan , Yujun Li , Yuanlin Zhang , Chunle Guo , Xiongxin Tang , Ruixing Wang , Chongyi Li

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

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes.…

Computer Vision and Pattern Recognition · Computer Science 2022-11-29 Kai-En Lin , Guowei Yang , Lei Xiao , Feng Liu , Ravi Ramamoorthi

Embedding a language field in a 3D representation enables richer semantic understanding of spatial environments by linking geometry with descriptive meaning. This allows for a more intuitive human-computer interaction, enabling querying or…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Shai Krakovsky , Gal Fiebelman , Sagie Benaim , Hadar Averbuch-Elor

Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to…

Computer Vision and Pattern Recognition · Computer Science 2025-11-14 Frédéric Berdoz , Luca A. Lanzendörfer , Nick Tuninga , Roger Wattenhofer

Recent advancements in differentiable rendering and 3D reasoning have driven exciting results in novel view synthesis from a single image. Despite realistic results, methods are limited to relatively small view change. In order to…

Computer Vision and Pattern Recognition · Computer Science 2021-08-13 Chris Rockwell , David F. Fouhey , Justin Johnson

Recognizing arbitrary or previously unseen categories is essential for comprehensive real-world 3D scene understanding. Currently, all existing methods rely on 2D or textual modalities during training or together at inference. This…

Computer Vision and Pattern Recognition · Computer Science 2025-06-04 Yue Li , Qi Ma , Runyi Yang , Huapeng Li , Mengjiao Ma , Bin Ren , Nikola Popovic , Nicu Sebe , Ender Konukoglu , Theo Gevers , Luc Van Gool , Martin R. Oswald , Danda Pani Paudel

Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task for immersive and embodied applications. The challenge lies in the lack of large-scale and high-quality real-world 3D scans…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Jie-Ying Lee , Yi-Ruei Liu , Shr-Ruei Tsai , Wei-Cheng Chang , Chung-Ho Wu , Jiewen Chan , Zhenjun Zhao , Chieh Hubert Lin , Yu-Lun Liu

We present a novel method for populating 3D indoor scenes with virtual humans that can navigate in the environment and interact with objects in a realistic manner. Existing approaches rely on training sequences that contain captured human…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Kaifeng Zhao , Yan Zhang , Shaofei Wang , Thabo Beeler , Siyu Tang

Generating artistic and coherent 3D scene layouts is crucial in digital content creation. Traditional optimization-based methods are often constrained by cumbersome manual rules, while deep generative models face challenges in producing…

Computer Vision and Pattern Recognition · Computer Science 2025-10-20 Xiaoming Zhu , Xu Huang , Qinghongbing Xie , Zhi Deng , Junsheng Yu , Yirui Guan , Zhongyuan Liu , Lin Zhu , Qijun Zhao , Ligang Liu , Long Zeng

3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven to be an effective strategy for 3D scene understanding.…

Computer Vision and Pattern Recognition · Computer Science 2026-01-27 Mengjiao Ma , Qi Ma , Yue Li , Jiahuan Cheng , Runyi Yang , Bin Ren , Nikola Popovic , Mingqiang Wei , Nicu Sebe , Luc Van Gool , Theo Gevers , Martin R. Oswald , Danda Pani Paudel

Scene text synthesis involves rendering specified texts onto arbitrary images. Current methods typically formulate this task in an end-to-end manner but lack effective character-level guidance during training. Besides, their text encoders,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Yibin Wang , Weizhong Zhang , Honghui Xu , Cheng Jin

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

World building with 3D scene representations is increasingly important for content creation, simulation, and interactive experiences, yet real workflows are inherently iterative: creators must repeatedly extend an existing scene under user…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Zijian He , Renjie Liu , Yihao Wang , Weizhi Zhong , Huan Yuan , Kun Gai , Guangrun Wang , Guanbin Li

Visual scene understanding is a fundamental task in computer vision that aims to extract meaningful information from visual data. It traditionally involves disjoint and specialized algorithms for different tasks that are tailored for…

Computer Vision and Pattern Recognition · Computer Science 2024-12-09 Américo Pereira , Pedro Carvalho , Luís Côrte-Real

Synthesis of diverse driving scenes serves as a crucial data augmentation technique for validating the robustness and generalizability of autonomous driving systems. Current methods aggregate high-definition (HD) maps and 3D bounding boxes…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Zhechao Wang , Yiming Zeng , Lufan Ma , Zeqing Fu , Chen Bai , Ziyao Lin , Cheng Lu
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