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Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language…

Computer Vision and Pattern Recognition · Computer Science 2024-11-18 Yian Wang , Xiaowen Qiu , Jiageng Liu , Zhehuan Chen , Jiting Cai , Yufei Wang , Tsun-Hsuan Wang , Zhou Xian , Chuang Gan

Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first…

Computer Vision and Pattern Recognition · Computer Science 2026-01-15 Longbin Ji , Xiaoxiong Liu , Junyuan Shang , Shuohuan Wang , Yu Sun , Hua Wu , Haifeng Wang

Mesh models have become increasingly accessible for numerous cities; however, the lack of realistic textures restricts their application in virtual urban navigation and autonomous driving. To address this, this paper proposes MeSS…

Computer Vision and Pattern Recognition · Computer Science 2026-01-06 Xuyang Chen , Zhijun Zhai , Kaixuan Zhou , Zengmao Wang , Jianan He , Dong Wang , Yanfeng Zhang , mingwei Sun , Rüdiger Westermann , Konrad Schindler , Liqiu Meng

3D scene generation conditioned on text prompts has significantly progressed due to the development of 2D diffusion generation models. However, the textual description of 3D scenes is inherently inaccurate and lacks fine-grained control…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Minglin Chen , Longguang Wang , Sheng Ao , Ye Zhang , Kai Xu , Yulan Guo

We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies. Current approaches to driving scene synthesis either…

There are two prevalent ways to constructing 3D scenes: procedural generation and 2D lifting. Among them, panorama-based 2D lifting has emerged as a promising technique, leveraging powerful 2D generative priors to produce immersive,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-31 Yukun Huang , Jiwen Yu , Yanning Zhou , Jianan Wang , Xintao Wang , Pengfei Wan , Xihui Liu

We introduce an approach to enhance the novel view synthesis from images taken from a freely moving camera. The introduced approach focuses on outdoor scenes where recovering accurate geometric scaffold and camera pose is challenging,…

Computer Vision and Pattern Recognition · Computer Science 2023-03-31 Nishant Jain , Suryansh Kumar , Luc Van Gool

In this paper, we present PanoDreamer, a novel method for producing a coherent 360{\deg} 3D scene from a single input image. Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Avinash Paliwal , Xilong Zhou , Andrii Tsarov , Nima Khademi Kalantari

The convergence of generative artificial intelligence and advanced computer vision technologies introduces a groundbreaking approach to transforming textual descriptions into three-dimensional representations. This research proposes a fully…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Venkat Kumar R , Deepak Saravanan

Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consistency in highly dynamic real-world environments. In this work, we introduce \textbf{AnyView}, a…

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. Additionally, 3D scene generation is vital for advancing embodied AI and world models, which depend…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Yuxin Zhang , Ziyu Lu , Hongbo Duan , Keyu Fan , Pengting Luo , Peiyu Zhuang , Mengyu Yang , Houde Liu

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

Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as in generating virtual robot training environments. This task…

Computer Vision and Pattern Recognition · Computer Science 2021-09-02 Haitao Yang , Zaiwei Zhang , Siming Yan , Haibin Huang , Chongyang Ma , Yi Zheng , Chandrajit Bajaj , Qixing Huang

This paper addresses the challenge of learning semantically and functionally meaningful 3D motion priors from real-world videos, in order to enable prediction of future 3D scene motion from a single input image. We propose a novel…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Jiahui Lei , Kyle Genova , George Kopanas , Noah Snavely , Leonidas Guibas

Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Sibo Wu , Congrong Xu , Binbin Huang , Andreas Geiger , Anpei Chen

With the development of deep neural networks, the demand for a significant amount of annotated training data becomes the performance bottlenecks in many fields of research and applications. Image synthesis can generate annotated images…

Computer Vision and Pattern Recognition · Computer Science 2020-12-24 Minghui Liao , Boyu Song , Shangbang Long , Minghang He , Cong Yao , Xiang Bai

We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacular results on 2D images, they have not yet achieved similar…

Computer Vision and Pattern Recognition · Computer Science 2022-07-26 Zuoyue Li , Tianxing Fan , Zhenqiang Li , Zhaopeng Cui , Yoichi Sato , Marc Pollefeys , Martin R. Oswald

Given just a few glimpses of a scene, can you imagine the movie playing out as the camera glides through it? That's the lens we take on \emph{sparse-input novel view synthesis}, not only as filling spatial gaps between widely spaced views,…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Yan Xu , Yixing Wang , Stella X. Yu

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

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