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Controllability plays a crucial role in video generation, as it allows users to create and edit content more precisely. Existing models, however, lack control of camera pose that serves as a cinematic language to express deeper narrative…

Computer Vision and Pattern Recognition · Computer Science 2025-03-17 Hao He , Yinghao Xu , Yuwei Guo , Gordon Wetzstein , Bo Dai , Hongsheng Li , Ceyuan Yang

Image generation today can produce somewhat realistic images from text prompts. However, if one asks the generator to synthesize a specific camera setting such as creating different fields of view using a 24mm lens versus a 70mm lens, the…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Yu Yuan , Xijun Wang , Yichen Sheng , Prateek Chennuri , Xingguang Zhang , Stanley Chan

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

Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical sets. To address this, we introduce the task of cinematic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Kaiyi Huang , Yukun Huang , Yu Li , Jianhong Bai , Xintao Wang , Zinan Lin , Xuefei Ning , Jiwen Yu , Pengfei Wan , Yu Wang , Xihui Liu

Camera control has been actively studied in text or image conditioned video generation tasks. However, altering camera trajectories of a given video remains under-explored, despite its importance in the field of video creation. It is…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Jianhong Bai , Menghan Xia , Xiao Fu , Xintao Wang , Lianrui Mu , Jinwen Cao , Zuozhu Liu , Haoji Hu , Xiang Bai , Pengfei Wan , Di Zhang

Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement. Accurate control of both camera and object motion is essential for video generation. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Zhouxia Wang , Ziyang Yuan , Xintao Wang , Tianshui Chen , Menghan Xia , Ping Luo , Ying Shan

Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transition capabilities remain rudimentary and unstable, largely…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Xiaoxue Wu , Bingjie Gao , Yu Qiao , Yaohui Wang , Xinyuan Chen

Cinemagraphs, which combine static photographs with selective, looping motion, offer unique artistic appeal. Generating them from a single photograph in a controllable manner is particularly challenging. Existing image-animation techniques…

Computer Vision and Pattern Recognition · Computer Science 2026-01-07 Aniruddha Mahapatra , Long Mai , Cusuh Ham , Feng Liu

Camera control is crucial for generating expressive and cinematic videos. Existing methods rely on explicit sequences of camera parameters as control conditions, which can be cumbersome for users to construct, particularly for intricate…

Computer Vision and Pattern Recognition · Computer Science 2025-06-04 Yawen Luo , Jianhong Bai , Xiaoyu Shi , Menghan Xia , Xintao Wang , Pengfei Wan , Di Zhang , Kun Gai , Tianfan Xue

We present GEN3C, a generative video model with precise Camera Control and temporal 3D Consistency. Prior video models already generate realistic videos, but they tend to leverage little 3D information, leading to inconsistencies, such as…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Xuanchi Ren , Tianchang Shen , Jiahui Huang , Huan Ling , Yifan Lu , Merlin Nimier-David , Thomas Müller , Alexander Keller , Sanja Fidler , Jun Gao

Interactive video generation has significant potential for scene simulation and video creation. However, existing methods often struggle with maintaining scene consistency during long video generation under dynamic camera control due to…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Xinhang Gao , Junlin Guan , Shuhan Luo , Wenzhuo Li , Guanghuan Tan , Jiacheng Wang

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

Camera control, which achieves diverse visual effects by changing camera position and pose, has attracted widespread attention. However, existing methods face challenges such as complex interaction and limited control capabilities. To…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Xiaoda Yang , Jiayang Xu , Kaixuan Luan , Xinyu Zhan , Hongshun Qiu , Shijun Shi , Hao Li , Shuai Yang , Li Zhang , Checheng Yu , Cewu Lu , Lixin Yang

High-fidelity generative video editing has seen significant quality improvements by leveraging pre-trained video foundation models. However, their computational cost is a major bottleneck, as they are often designed to inefficiently process…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Yehonathan Litman , Shikun Liu , Dario Seyb , Nicholas Milef , Yang Zhou , Carl Marshall , Shubham Tulsiani , Caleb Leak

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

User-generated cinematic creations are gaining popularity as our daily entertainment, yet it is a challenge to master cinematography for producing immersive contents. Many existing automatic methods focus on roughly controlling predefined…

Multimedia · Computer Science 2024-05-24 Xinyi Wu , Haohong Wang , Aggelos K. Katsaggelos

We present a unified controllable video generation approach AnimateAnything that facilitates precise and consistent video manipulation across various conditions, including camera trajectories, text prompts, and user motion annotations.…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Guojun Lei , Chi Wang , Hong Li , Rong Zhang , Yikai Wang , Weiwei Xu

Simulation is crucial for developing and evaluating autonomous vehicle (AV) systems. Recent literature builds on a new generation of generative models to synthesize highly realistic images for full-stack simulation. However, purely…

Computer Vision and Pattern Recognition · Computer Science 2025-06-25 Zehao Zhu , Yuliang Zou , Chiyu Max Jiang , Bo Sun , Vincent Casser , Xiukun Huang , Jiahao Wang , Zhenpei Yang , Ruiqi Gao , Leonidas Guibas , Mingxing Tan , Dragomir Anguelov

As generative models become ubiquitous, there is a critical need for fine-grained control over the generation process. Yet, while controlled generation methods from prompting to fine-tuning proliferate, a fundamental question remains…

Artificial Intelligence · Computer Science 2026-01-12 Emily Cheng , Carmen Amo Alonso , Federico Danieli , Arno Blaas , Luca Zappella , Pau Rodriguez , Xavier Suau

This paper presents DualCamCtrl, a novel end-to-end diffusion model for camera-controlled video generation. Recent works have advanced this field by representing camera poses as ray-based conditions, yet they often lack sufficient scene…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Hongfei Zhang , Kanghao Chen , Zixin Zhang , Harold Haodong Chen , Yuanhuiyi Lyu , Yuqi Zhang , Shuai Yang , Kun Zhou , Yingcong Chen
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