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Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often do not offer precise control over camera poses for video…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Dejia Xu , Weili Nie , Chao Liu , Sifei Liu , Jan Kautz , Zhangyang Wang , Arash Vahdat

Recent advancements in camera-trajectory-guided image-to-video generation offer higher precision and better support for complex camera control compared to text-based approaches. However, they also introduce significant usability challenges,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Teng Li , Guangcong Zheng , Rui Jiang , Shuigen Zhan , Tao Wu , Yehao Lu , Yining Lin , Chuanyun Deng , Yepan Xiong , Min Chen , Lin Cheng , Xi Li

Camera control has been extensively studied in conditioned video generation; however, performing precisely altering the camera trajectories while faithfully preserving the video content remains a challenging task. The mainstream approach to…

Computer Vision and Pattern Recognition · Computer Science 2026-02-02 Dong-Yu Chen , Yixin Guo , Shuojin Yang , Tai-Jiang Mu , Shi-Min Hu

Incorporating camera intrinsics into video generation models offers a principled way to control not only scene dynamics but also the imaging process that governs visual appearance. Prior work has primarily focused on extrinsic control, such…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Debabrata Mandal , Zhihan Peng , Yujie Wang , Praneeth Chakravarthula

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

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

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Cong Wang , Jiaxi Gu , Panwen Hu , Songcen Xu , Hang Xu , Xiaodan Liang

Video generation with controllable camera viewpoints is essential for applications such as interactive content creation, gaming, and simulation. Existing methods typically adapt pre-trained video models using camera poses relative to a…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Chunyang Li , Yuanbo Yang , Jiahao Shao , Hongyu Zhou , Katja Schwarz , Yiyi Liao

For artistic applications, video generation requires fine-grained control over both performance and cinematography, i.e., the actor's motion and the camera trajectory. We present ActCam, a zero-shot method for video generation that jointly…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Omar El Khalifi , Thomas Rossi , Oscar Fossey , Thibault Fouque , Ulysse Mizrahi , Philip Torr , Ivan Laptev , Fabio Pizzati , Baptiste Bellot-Gurlet

Controllability, temporal coherence, and detail synthesis remain the most critical challenges in video generation. In this paper, we focus on a commonly used yet underexplored cinematic technique known as Frame In and Frame Out.…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Boyang Wang , Xuweiyi Chen , Matheus Gadelha , Zezhou Cheng

Precise camera pose control is crucial for video generation with diffusion models. Existing methods require fine-tuning with additional datasets containing paired videos and camera pose annotations, which are both data-intensive and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Zhenghong Zhou , Jie An , Jiebo Luo

Camera-controlled video-to-video (V2V) generation enables dynamic viewpoint synthesis from monocular footage, holding immense potential for interactive filmmaking and live broadcasting. However, existing implicit synthesis methods…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Youcan Xu , Jiaxin Shi , Zhen Wang , Wensong Song , Feifei Shao , Chen Liang , Jun Xiao , Long Chen

We introduce FaceCam, a system that generates video under customizable camera trajectories for monocular human portrait video input. Recent camera control approaches based on large video-generation models have shown promising progress but…

Computer Vision and Pattern Recognition · Computer Science 2026-03-06 Weijie Lyu , Ming-Hsuan Yang , Zhixin Shu

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

We propose PostCam, a framework for novel-view video generation that enables post-capture editing of camera trajectories in dynamic scenes. We find that existing video recapture methods suffer from suboptimal camera motion injection…

Computer Vision and Pattern Recognition · Computer Science 2025-11-24 Yipeng Chen , Zhichao Ye , Zhenzhou Fang , Xinyu Chen , Xiaoyu Zhang , Jialing Liu , Nan Wang , Haomin Liu , Guofeng Zhang

Recently, camera-controlled video generation has seen rapid development, offering more precise control over video generation. However, existing methods predominantly focus on camera control in perspective projection video generation, while…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Chenhao Ji , Chaohui Yu , Junyao Gao , Fan Wang , Cairong Zhao

Video is a rich and scalable source of 3D/4D visual observations, and camera control is a key capability for video generation models to produce geometrically meaningful content. Existing approaches typically learn a mapping from camera…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Chen Hou , Christian Rupprecht

Over recent years, diffusion models have facilitated significant advancements in video generation. Yet, the creation of face-related videos still confronts issues such as low facial fidelity, lack of frame consistency, limited editability…

Computer Vision and Pattern Recognition · Computer Science 2023-12-22 Linze Li , Sunqi Fan , Hengjun Pu , Zhaodong Bing , Yao Tang , Tianzhu Ye , Tong Yang , Liangyu Chen , Jiajun Liang

Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency. This progress inspires us to investigate the potential of these models to ensure dynamic…

Computer Vision and Pattern Recognition · Computer Science 2024-12-11 Jianhong Bai , Menghan Xia , Xintao Wang , Ziyang Yuan , Xiao Fu , Zuozhu Liu , Haoji Hu , Pengfei Wan , Di Zhang

Video fundamentally intertwines two crucial axes: the dynamic content of a scene and the camera motion through which it is observed. However, existing generation models often entangle these factors, limiting independent control. In this…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Yukun Wang , Ruihuang Li , Jiale Tao , Shiyuan Yang , Liyi Chen , Zhantao Yang , Handz , Yulan Guo , Shuai Shao , Qinglin Lu
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