English
Related papers

Related papers: CAT4D: Create Anything in 4D with Multi-View Video…

200 papers

Existing 4D synthesis methods primarily focus on object-level generation or dynamic scene synthesis with limited novel views, restricting their ability to generate multi-view consistent and immersive dynamic 4D scenes. To address these…

Computer Vision and Pattern Recognition · Computer Science 2025-06-25 Junwei Zhou , Xueting Li , Lu Qi , Ming-Hsuan Yang

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion…

Computer Vision and Pattern Recognition · Computer Science 2022-05-04 Tianye Li , Mira Slavcheva , Michael Zollhoefer , Simon Green , Christoph Lassner , Changil Kim , Tanner Schmidt , Steven Lovegrove , Michael Goesele , Richard Newcombe , Zhaoyang Lv

We introduce a novel method for dynamic free-view synthesis of an ambient scenes from a monocular capture bringing a immersive quality to the viewing experience. Our method builds upon the recent advancements in 3D Gaussian Splatting (3DGS)…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Meng-Li Shih , Jia-Bin Huang , Changil Kim , Rajvi Shah , Johannes Kopf , Chen Gao

Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2024-11-06 Yuyang Zhao , Chung-Ching Lin , Kevin Lin , Zhiwen Yan , Linjie Li , Zhengyuan Yang , Jianfeng Wang , Gim Hee Lee , Lijuan Wang

We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras. Key to our approach is the use of self-supervised neural networks specific to the scene to compose…

Computer Vision and Pattern Recognition · Computer Science 2020-05-28 Aayush Bansal , Minh Vo , Yaser Sheikh , Deva Ramanan , Srinivasa Narasimhan

Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized…

Computer Vision and Pattern Recognition · Computer Science 2024-02-21 Xiaoming Zhao , Alex Colburn , Fangchang Ma , Miguel Angel Bautista , Joshua M. Susskind , Alexander G. Schwing

Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images. However, the challenging problem of text-to-4D dynamic 3D scene generation with diffusion…

Computer Vision and Pattern Recognition · Computer Science 2024-05-08 Yufeng Zheng , Xueting Li , Koki Nagano , Sifei Liu , Karsten Kreis , Otmar Hilliges , Shalini De Mello

We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our method re-synthesizes the scene with the same dynamics from a…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Kuan Heng Lin , Zhizheng Liu , Pablo Salamanca , Yash Kant , Ryan Burgert , Yuancheng Xu , Koichi Namekata , Yiwei Zhao , Bolei Zhou , Micah Goldblum , Paul Debevec , Ning Yu

In this paper, we present MVTokenFlow for high-quality 4D content creation from monocular videos. Recent advancements in generative models such as video diffusion models and multiview diffusion models enable us to create videos or 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-02-18 Hanzhuo Huang , Yuan Liu , Ge Zheng , Jiepeng Wang , Zhiyang Dou , Sibei Yang

The synthesis of spatiotemporally coherent 4D content presents fundamental challenges in computer vision, requiring simultaneous modeling of high-fidelity spatial representations and physically plausible temporal dynamics. Current…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Xiaoyan Liu , Kangrui Li , Yuehao Song , Jiaxin Liu

Novel view synthesis from an in-the-wild video is difficult due to challenges like scene dynamics and lack of parallax. While existing methods have shown promising results with implicit neural radiance fields, they are slow to train and…

Computer Vision and Pattern Recognition · Computer Science 2024-07-22 Yao-Chih Lee , Zhoutong Zhang , Kevin Blackburn-Matzen , Simon Niklaus , Jianming Zhang , Jia-Bin Huang , Feng Liu

In this paper, we propose NeoVerse, a versatile 4D world model that is capable of 4D reconstruction, novel-trajectory video generation, and rich downstream applications. We first identify a common limitation of scalability in current 4D…

Computer Vision and Pattern Recognition · Computer Science 2026-03-27 Yuxue Yang , Lue Fan , Ziqi Shi , Junran Peng , Feng Wang , Zhaoxiang Zhang

Recent breakthroughs in video generation, powered by large-scale datasets and diffusion techniques, have shown that video diffusion models can function as implicit 4D novel view synthesizers. Nevertheless, current methods primarily…

Computer Vision and Pattern Recognition · Computer Science 2025-10-09 Yihao Zhi , Chenghong Li , Hongjie Liao , Xihe Yang , Zhengwentai Sun , Jiahao Chang , Xiaodong Cun , Wensen Feng , Xiaoguang Han

Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-world scenes from monocular real-world videos. Our Dynamic Scene…

Computer Vision and Pattern Recognition · Computer Science 2024-03-18 Maximilian Seitzer , Sjoerd van Steenkiste , Thomas Kipf , Klaus Greff , Mehdi S. M. Sajjadi

Dynamic reconstruction and spatiotemporal novel-view synthesis of non-rigidly deforming scenes recently gained increased attention. While existing work achieves impressive quality and performance on multi-view or teleporting camera setups,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-03 Moritz Kappel , Florian Hahlbohm , Timon Scholz , Susana Castillo , Christian Theobalt , Martin Eisemann , Vladislav Golyanik , Marcus Magnor

Recovering 4D from monocular video, which jointly estimates dynamic geometry and camera poses, is an inevitably challenging problem. While recent pointmap-based 3D reconstruction methods (e.g., DUSt3R) have made great progress in…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Shizun Wang , Zhenxiang Jiang , Xingyi Yang , Xinchao Wang

We explore novel-view synthesis for dynamic scenes from monocular videos. Prior approaches rely on costly test-time optimization of 4D representations or do not preserve scene geometry when trained in a feed-forward manner. Our approach is…

Computer Vision and Pattern Recognition · Computer Science 2026-01-14 Kaihua Chen , Tarasha Khurana , Deva Ramanan

We introduce 3D4D, an interactive 4D visualization framework that integrates WebGL with Supersplat rendering. It transforms static images and text into coherent 4D scenes through four core modules and employs a foveated rendering strategy…

Computer Vision and Pattern Recognition · Computer Science 2025-11-12 Yunhong He , Zhengqing Yuan , Zhengzhong Tu , Yanfang Ye , Lichao Sun

This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Zhen Xu , Sida Peng , Haotong Lin , Guangzhao He , Jiaming Sun , Yujun Shen , Hujun Bao , Xiaowei Zhou

We introduce VividDream, a method for generating explorable 4D scenes with ambient dynamics from a single input image or text prompt. VividDream first expands an input image into a static 3D point cloud through iterative inpainting and…

Computer Vision and Pattern Recognition · Computer Science 2024-05-31 Yao-Chih Lee , Yi-Ting Chen , Andrew Wang , Ting-Hsuan Liao , Brandon Y. Feng , Jia-Bin Huang