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High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Weihong Pan , Xiaoyu Zhang , Zhuang Zhang , Zhichao Ye , Nan Wang , Haomin Liu , Guofeng Zhang

Reconstructing dynamic 4D scenes is an important yet challenging task. While 3D foundation models like VGGT excel in static settings, they often struggle with dynamic sequences where motion causes significant geometric ambiguity. To address…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Ying Zang , Yidong Han , Chaotao Ding , Yuanqi Hu , Deyi Ji , Qi Zhu , Xuanfu Li , Jin Ma , Lingyun Sun , Tianrun Chen , Lanyun Zhu

Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic…

Computer Vision and Pattern Recognition · Computer Science 2020-03-11 Chenjie Wang , Bin Luo , Yun Zhang , Qing Zhao , Lu Yin , Wei Wang , Xin Su , Yajun Wang , Chengyuan Li

World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent video generation models have demonstrated impressive visual…

Reconstructing dynamic 4D scenes is challenging, as it requires robust disentanglement of dynamic objects from the static background. While 3D foundation models like VGGT provide accurate 3D geometry, their performance drops markedly when…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yu Hu , Chong Cheng , Sicheng Yu , Xiaoyang Guo , Hao Wang

4D Gaussian Splatting (4DGS) has recently emerged as a promising technique for capturing complex dynamic 3D scenes with high fidelity. It utilizes a 4D Gaussian representation and a GPU-friendly rasterizer, enabling rapid rendering speeds.…

Computer Vision and Pattern Recognition · Computer Science 2025-07-23 Xinjie Zhang , Zhening Liu , Yifan Zhang , Xingtong Ge , Dailan He , Tongda Xu , Yan Wang , Zehong Lin , Shuicheng Yan , Jun Zhang

We present Motion 3-to-4, a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video and an optional 3D reference mesh. While recent advances have significantly improved 2D, video, and 3D content…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Hongyuan Chen , Xingyu Chen , Youjia Zhang , Zexiang Xu , Anpei Chen

Recent advances in foundational Video Diffusion Models (VDMs) have yielded significant progress. Yet, despite the remarkable visual quality of generated videos, reconstructing consistent 3D scenes from these outputs remains challenging, due…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Yisu Zhang , Chenjie Cao , Tengfei Wang , Xuhui Zuo , Junta Wu , Jianke Zhu , Chunchao Guo

Reconstructing dynamic 3D scenes from sparse multi-view videos is highly ill-posed, often leading to geometric collapse, trajectory drift, and floating artifacts. Recent attempts introduce generative priors to hallucinate missing content,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Zhenlong Wu , Zihan Zheng , Xuanxuan Wang , Qianhe Wang , Hua Yang , Xiaoyun Zhang , Qiang Hu , Wenjun Zhang

We study the problem of synthesizing a long-term dynamic video from only a single image. This is challenging since it requires consistent visual content movements given large camera motions. Existing methods either hallucinate inconsistent…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Liao Shen , Xingyi Li , Huiqiang Sun , Juewen Peng , Ke Xian , Zhiguo Cao , Guosheng Lin

Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and reconstruct-then-generate methods decouple geometry from motion, causing spatiotemporal…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Yanran Zhang , Ziyi Wang , Wenzhao Zheng , Zheng Zhu , Jie Zhou , Jiwen Lu

This paper tackles the challenge of recovering 4D dynamic scenes from videos captured by as few as four portable cameras. Learning to model scene dynamics for temporally consistent novel-view rendering is a foundational task in computer…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Junsheng Zhou , Zhifan Yang , Liang Han , Wenyuan Zhang , Kanle Shi , Shenkun Xu , Yu-Shen Liu

This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras.…

Computer Vision and Pattern Recognition · Computer Science 2016-03-29 Armin Mustafa , Hansung Kim , Jean-Yves Guillemaut , Adrian Hilton

The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant…

Computer Vision and Pattern Recognition · Computer Science 2024-11-21 Luoxi Zhang , Pragyan Shrestha , Yu Zhou , Chun Xie , Itaru Kitahara

Reconstructing dynamic 4D scenes remains challenging due to the presence of moving objects that corrupt camera pose estimation. Existing optimization methods alleviate this issue with additional supervision, but they are mostly…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Juntong Fang , Zequn Chen , Weiqi Zhang , Donglin Di , Xuancheng Zhang , Chengmin Yang , Yu-Shen Liu

Reconstructing general dynamic scenes is important for many computer vision and graphics applications. Recent works represent the dynamic scene with neural radiance fields for photorealistic view synthesis, while their surface geometry is…

Computer Vision and Pattern Recognition · Computer Science 2023-03-02 Decai Chen , Haofei Lu , Ingo Feldmann , Oliver Schreer , Peter Eisert

Reconstructing and tracking dynamic 3D scenes remains a fundamental challenge in computer vision. Existing approaches often decouple geometry from motion: multi-view reconstruction methods assume static scenes, while dynamic tracking…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Shenhan Qian , Ganlin Zhang , Shangzhe Wu , Daniel Cremers

4D generation, or dynamic 3D content generation, integrates spatial, temporal, and view dimensions to model realistic dynamic scenes, playing a foundational role in advancing world models and physical AI. However, maintaining long-chain…

Graphics · Computer Science 2026-04-01 Yuanbin Man , Ying Huang , Zhile Ren , Miao Yin

Novel view synthesis from monocular videos of dynamic scenes with unknown camera poses remains a fundamental challenge in computer vision and graphics. While recent advances in 3D representations such as Neural Radiance Fields (NeRF) and 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-11-10 Mengqi Guo , Bo Xu , Yanyan Li , Gim Hee Lee

Dynamical systems are ubiquitous within science and engineering, from turbulent flow across aircraft wings to structural variability of proteins. Although some systems are well understood and simulated, scientific imaging often confronts…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Ali SaraerToosi , Renbo Tu , Kamyar Azizzadenesheli , Aviad Levis