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Related papers: Choreographing a World of Dynamic Objects

200 papers

Recent advances in 3D scene editing using NeRF and 3DGS enable high-quality static scene editing. In contrast, dynamic scene editing remains challenging, as methods that directly extend 2D diffusion models to 4D often produce motion…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Shifeng Chen , Yihui Li , Jun Liao , Hongyu Yang , Di Huang

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph…

Computer Vision and Pattern Recognition · Computer Science 2017-08-11 Tae-Hyun Oh , Kyungdon Joo , Neel Joshi , Baoyuan Wang , In So Kweon , Sing Bing Kang

Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content. However, the scarcity of 3D scene datasets constrains current methodologies to primarily object-centric…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Dejia Xu , Hanwen Liang , Neel P. Bhatt , Hezhen Hu , Hanxue Liang , Konstantinos N. Plataniotis , Zhangyang Wang

Generating coherent and useful image/video scenes from a free-form textual description is technically a very difficult problem to handle. Textual description of the same scene can vary greatly from person to person, or sometimes even for…

Computer Vision and Pattern Recognition · Computer Science 2020-12-01 Faria Huq , Nafees Ahmed , Anindya Iqbal

Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions…

Robotics · Computer Science 2026-01-01 Karthik Dharmarajan , Wenlong Huang , Jiajun Wu , Li Fei-Fei , Ruohan Zhang

Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches often use existing motion extracted from other videos, which…

Computer Vision and Pattern Recognition · Computer Science 2024-12-18 Hsin-Ping Huang , Yang Zhou , Jui-Hsien Wang , Difan Liu , Feng Liu , Ming-Hsuan Yang , Zhan Xu

Learning object-centric representations from unsupervised videos is challenging. Unlike most previous approaches that focus on decomposing 2D images, we present a 3D generative model named DynaVol-S for dynamic scenes that enables…

Computer Vision and Pattern Recognition · Computer Science 2025-02-17 Yanpeng Zhao , Yiwei Hao , Siyu Gao , Yunbo Wang , Xiaokang Yang

A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background, but without considering the underlying 3D scene that gives…

Computer Vision and Pattern Recognition · Computer Science 2021-03-26 Paul Henderson , Christoph H. Lampert

Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle…

Computer Vision and Pattern Recognition · Computer Science 2021-04-30 Michael Niemeyer , Andreas Geiger

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Haozhe Xie , Zhaoxi Chen , Fangzhou Hong , Ziwei Liu

Recent advances in diffusion models have revolutionized 2D and 3D content creation, yet generating photorealistic dynamic 4D scenes remains a significant challenge. Existing dynamic 4D generation methods typically rely on distilling…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Vinayak Gupta , Yunze Man , Yu-Xiong Wang

Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on neural radiance field or 3D Gaussian splatting are limited…

Computer Vision and Pattern Recognition · Computer Science 2025-07-04 Jiahao Wu , Rui Peng , Jianbo Jiao , Jiayu Yang , Luyang Tang , Kaiqiang Xiong , Jie Liang , Jinbo Yan , Runling Liu , Ronggang Wang

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

Despite recent advancements in single-domain or single-object image generation, it is still challenging to generate complex scenes containing diverse, multiple objects and their interactions. Scene graphs, composed of nodes as objects and…

Computer Vision and Pattern Recognition · Computer Science 2021-08-13 Sarthak Garg , Helisa Dhamo , Azade Farshad , Sabrina Musatian , Nassir Navab , Federico Tombari

Realistic simulation of dynamic scenes requires accurately capturing diverse material properties and modeling complex object interactions grounded in physical principles. However, existing methods are constrained to basic material types…

Computer Vision and Pattern Recognition · Computer Science 2025-05-09 Zhuoman Liu , Weicai Ye , Yan Luximon , Pengfei Wan , Di Zhang

Spatio-temporal scene graphs provide a principled representation for modeling evolving object interactions, yet existing methods remain fundamentally frame-centric: they reason only about currently visible objects, discard entities upon…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Rohith Peddi , Saurabh , Shravan Shanmugam , Likhitha Pallapothula , Yu Xiang , Parag Singla , Vibhav Gogate

Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Chen Wang , Chuhao Chen , Yiming Huang , Zhiyang Dou , Yuan Liu , Jiatao Gu , Lingjie Liu

Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Jiraphon Yenphraphai , Ashkan Mirzaei , Jianqi Chen , Jiaxu Zou , Sergey Tulyakov , Raymond A. Yeh , Peter Wonka , Chaoyang Wang

Modeling scenes using video generation models has garnered growing research interest in recent years. However, most existing approaches rely on perspective video models that synthesize only limited observations of a scene, leading to issues…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Yuheng Liu , Xin Lin , Xinke Li , Baihan Yang , Chen Wang , Kalyan Sunkavalli , Yannick Hold-Geoffroy , Hao Tan , Kai Zhang , Xiaohui Xie , Zifan Shi , Yiwei Hu