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A growing number of visual computing applications depend on the analysis of large video collections. The challenge is that scaling applications to operate on these datasets requires efficient systems for pixel data access and parallel…

Computer Vision and Pattern Recognition · Computer Science 2018-05-21 Alex Poms , Will Crichton , Pat Hanrahan , Kayvon Fatahalian

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior works, SceneDiffuser is…

Computer Vision and Pattern Recognition · Computer Science 2023-01-18 Siyuan Huang , Zan Wang , Puhao Li , Baoxiong Jia , Tengyu Liu , Yixin Zhu , Wei Liang , Song-Chun Zhu

Real-world video super-resolution (VSR) presents significant challenges due to complex and unpredictable degradations. Although some recent methods utilize image diffusion models for VSR and have shown improved detail generation…

Computer Vision and Pattern Recognition · Computer Science 2025-07-02 Zhe Kong , Le Li , Yong Zhang , Feng Gao , Shaoshu Yang , Tao Wang , Kaihao Zhang , Zhuoliang Kang , Xiaoming Wei , Guanying Chen , Wenhan Luo

The increasing demand for AR/VR applications has highlighted the need for high-quality content, such as 360{\deg} live wallpapers. However, generating high-quality 360{\deg} panoramic contents remains a challenging task due to the severe…

Computer Vision and Pattern Recognition · Computer Science 2025-11-14 Minho Park , Taewoong Kang , Jooyeol Yun , Sungwon Hwang , Jaegul Choo

360$^\circ$ video saliency detection is one of the challenging benchmarks for 360$^\circ$ video understanding since non-negligible distortion and discontinuity occur in the projection of any format of 360$^\circ$ videos, and capture-worthy…

Computer Vision and Pattern Recognition · Computer Science 2022-09-20 Heeseung Yun , Sehun Lee , Gunhee Kim

Recent advances in image generation have led to remarkable improvements in synthesizing perspective images. However, these models still struggle with panoramic image generation due to unique challenges, including varying levels of geometric…

Computer Vision and Pattern Recognition · Computer Science 2025-06-30 Hakan Çapuk , Andrew Bond , Muhammed Burak Kızıl , Emir Göçen , Erkut Erdem , Aykut Erdem

Being able to edit panoramic images is crucial for creating realistic 360{\deg} visual experiences. However, existing perspective-based image editing methods fail to model the spatial structure of panoramas. Conventional cube-map…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Dong Liang , Yuhao Liu , Jinyuan Jia , Youjun Zhao , Rynson W. H. Lau

The generation of immersive and navigable 3D environments is increasingly prevalent with the growing adoption of virtual reality and 3D content. However, recent methods face a fundamental limitation: they cannot produce 3D worlds that…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Antoine Schnepf , Karim Kassab , Flavian Vasile , Andrew Comport

Novel view synthesis (NVS) from a single image is highly ill-posed due to large unobserved regions, especially for views that deviate significantly from the input. While existing methods focus on consistency between the source and generated…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Xueyang Kang , Zhengkang Xiang , Zezheng Zhang , Kourosh Khoshelham

This is a technical report on the 360-degree panoramic image generation task based on diffusion models. Unlike ordinary 2D images, 360-degree panoramic images capture the entire $360^\circ\times 180^\circ$ field of view. So the rightmost…

Computer Vision and Pattern Recognition · Computer Science 2023-11-23 Mengyang Feng , Jinlin Liu , Miaomiao Cui , Xuansong Xie

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal…

Computer Vision and Pattern Recognition · Computer Science 2023-05-04 Nikita Karaev , Ignacio Rocco , Benjamin Graham , Natalia Neverova , Andrea Vedaldi , Christian Rupprecht

Interactive 3D scene generation from a single image has gained significant attention due to its potential to create immersive virtual worlds. However, a key challenge in current 3D generation methods is the limited explorability, which…

Computer Vision and Pattern Recognition · Computer Science 2025-06-26 Chaojun Ni , Jie Li , Haoyun Li , Hengyu Liu , Xiaofeng Wang , Zheng Zhu , Guosheng Zhao , Boyuan Wang , Chenxin Li , Guan Huang , Wenjun Mei

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

Video diffusion models have revolutionized generative video synthesis, but they are imprecise, slow, and can be opaque during generation -- keeping users in the dark for a prolonged period. In this work, we propose DiffusionBrowser, a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Susung Hong , Chongjian Ge , Zhifei Zhang , Jui-Hsien Wang

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

High-quality video generation is crucial for many fields, including the film industry and autonomous driving. However, generating videos with spatiotemporal consistencies remains challenging. Current methods typically utilize attention…

Computer Vision and Pattern Recognition · Computer Science 2025-04-28 Haotian Dong , Xin Wang , Di Lin , Yipeng Wu , Qin Chen , Ruonan Liu , Kairui Yang , Ping Li , Qing Guo

This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model. Previous camera-conditioned video generative models suffer from diminished video dynamics…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Hao He , Ceyuan Yang , Shanchuan Lin , Yinghao Xu , Meng Wei , Liangke Gui , Qi Zhao , Gordon Wetzstein , Lu Jiang , Hongsheng Li

Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in image synthesis. These models excel not only in creating fixed-size images but also in producing panoramic images. However,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Xiaoyu Zhang , Teng Zhou , Xinlong Zhang , Jia Wei , Yongchuan Tang

Generating 3D worlds from text is a highly anticipated goal in computer vision. Existing works are limited by the degree of exploration they allow inside of a scene, i.e., produce streched-out and noisy artifacts when moving beyond central…

Computer Vision and Pattern Recognition · Computer Science 2025-09-17 Manuel-Andreas Schneider , Lukas Höllein , Matthias Nießner

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