English
Related papers

Related papers: DreamGaussian4D: Generative 4D Gaussian Splatting

200 papers

Existing dynamic scene reconstruction methods based on Gaussian Splatting enable real-time rendering and generate realistic images. However, adjusting the camera's focal length or the distance between Gaussian primitives and the camera to…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Zilong Chen , Huan-ang Gao , Delin Qu , Haohan Chi , Hao Tang , Kai Zhang , Hao Zhao

Multi-view video reconstruction plays a vital role in computer vision, enabling applications in film production, virtual reality, and motion analysis. While recent advances such as 4D Gaussian Splatting (4DGS) have demonstrated impressive…

Computer Vision and Pattern Recognition · Computer Science 2025-11-17 Zhixin Xu , Hengyu Zhou , Yuan Liu , Wenhan Xue , Hao Pan , Wenping Wang , Bin Wang

Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a critical step towards scalable training of self-driving models. Reconstruction-based methods create 3D scenes from driving logs and synthesize…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Jiageng Mao , Boyi Li , Boris Ivanovic , Yuxiao Chen , Yan Wang , Yurong You , Chaowei Xiao , Danfei Xu , Marco Pavone , Yue Wang

We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D geometry or mediocre rendering quality due to improper…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Yuxuan Mu , Xinxin Zuo , Chuan Guo , Yilin Wang , Juwei Lu , Xiaofeng Wu , Songcen Xu , Peng Dai , Youliang Yan , Li Cheng

We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Zhibing Li , Mengchen Zhang , Tong Wu , Jing Tan , Jiaqi Wang , Dahua Lin

Recent advancements in foundation models for 2D vision have substantially improved the analysis of dynamic scenes from monocular videos. However, despite their strong generalization capabilities, these models often lack 3D consistency, a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Haoran Zhou , Gim Hee Lee

3D Gaussian Splatting is emerging as a state-of-the-art technique in novel view synthesis, recognized for its impressive balance between visual quality, speed, and rendering efficiency. However, reliance on third-degree spherical harmonics…

Computer Vision and Pattern Recognition · Computer Science 2025-01-03 Yiwen Wang , Siyuan Chen , Ran Yi

Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. An intuitive approach is to extend previous image-to-3D pipelines by transferring off-the-shelf image generation models such as score…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Zijie Pan , Zeyu Yang , Xiatian Zhu , Li Zhang

Modeling dynamic scenes through 4D Gaussians offers high visual fidelity and fast rendering speeds, but comes with significant storage overhead. Recent approaches mitigate this cost by aggressively reducing the number of Gaussians. However,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-06 Woong Oh Cho , In Cho , Seoha Kim , Jeongmin Bae , Youngjung Uh , Seon Joo Kim

Single-image 3D scene reconstruction presents significant challenges due to its inherently ill-posed nature and limited input constraints. Recent advances have explored two promising directions: multiview generative models that train on 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Junlin Hao , Peiheng Wang , Haoyang Wang , Xinggong Zhang , Zongming Guo

While the generation of 3D content from single-view images has been extensively studied, the creation of physically consistent 3D dynamic scenes from videos remains in its early stages. We propose a novel framework leveraging generative 3D…

Graphics · Computer Science 2025-10-31 Zhiwei Zhao , Alan Zhao , Minchen Li , Yixin Hu

While 3D Gaussian Splatting (3DGS) has revolutionized real-time photorealistic view synthesis, its fundamental reliance on symmetric Gaussian distributions introduces visual artifacts that hinder accurate spatial data exploration.…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Beizhen Zhao , Yifan Zhou , Gaochao Song , Ziran Yin , Hao Wang

3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis with high-quality rendering through continuous aggregations of millions of 3D Gaussian primitives. However, it suffers from a substantial memory footprint, particularly…

Computer Vision and Pattern Recognition · Computer Science 2026-04-27 Yangming Zhang , Jian Xu , Chaojian Li , Kunxiong Zhu , Wei Niu , Gagan Agrawal , Yang Katie Zhao , Jian Wang , Yingyan Celine Lin , Miao Yin

Generating dynamic 4D objects from sparse inputs is difficult because it demands joint preservation of appearance and motion coherence across views and time while suppressing artifacts and temporal drift. We hypothesize that the view…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Su Sun , Cheng Zhao , Himangi Mittal , Gaurav Mittal , Rohith Kukkala , Yingjie Victor Chen , Mei Chen

Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches, such as 3D Gaussian Splatting (3DGS), have emerged as state-of-the-art solutions, offering…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Ankit Dhiman , Tao Lu , R Srinath , Emre Arslan , Angela Xing , Yuanbo Xiangli , R Venkatesh Babu , Srinath Sridhar

Generating synthetic images is a useful method for cheaply obtaining labeled data for training computer vision models. However, obtaining accurate 3D models of relevant objects is necessary, and the resulting images often have a gap in…

Computer Vision and Pattern Recognition · Computer Science 2025-04-14 Bram Vanherle , Brent Zoomers , Jeroen Put , Frank Van Reeth , Nick Michiels

Traditional SLAM algorithms excel at camera tracking, but typically produce incomplete and low-resolution maps that are not tightly integrated with semantics prediction. Recent work integrates Gaussian Splatting (GS) into SLAM to enable…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Mingqi Jiang , Chanho Kim , Chen Ziwen , Li Fuxin

Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Haoyu Wu , Meher Gitika Karumuri , Chuhang Zou , Seungbae Bang , Yuelong Li , Dimitris Samaras , Sunil Hadap

Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typically adopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uniformly allocate Gaussians, leading to…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Injae Kim , Chaehyeon Kim , Minseong Bae , Minseok Joo , Hyunwoo J. Kim

Dynamic scenes rendering is an intriguing yet challenging problem. Although current methods based on NeRF have achieved satisfactory performance, they still can not reach real-time levels. Recently, 3D Gaussian Splatting (3DGS) has garnered…

Computer Vision and Pattern Recognition · Computer Science 2024-11-07 Jiahao Lu , Jiacheng Deng , Ruijie Zhu , Yanzhe Liang , Wenfei Yang , Tianzhu Zhang , Xu Zhou
‹ Prev 1 4 5 6 7 8 10 Next ›