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 nuances. To alleviate this issue, we introduce CameraCtrl, enabling accurate camera pose control for video diffusion models. Our approach explores effective camera trajectory parameterization along with a plug-and-play camera pose control module that is trained on top of a video diffusion model, leaving other modules of the base model untouched. Moreover, a comprehensive study on the effect of various training datasets is conducted, suggesting that videos with diverse camera distributions and similar appearance to the base model indeed enhance controllability and generalization. Experimental results demonstrate the effectiveness of CameraCtrl in achieving precise camera control with different video generation models, marking a step forward in the pursuit of dynamic and customized video storytelling from textual and camera pose inputs.
ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation
University of Freiburg（弗赖堡大学）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Technical University of Munich（慕尼黑技术大学）；Huawei Heisenberg Research Center (Munich)（华为海森堡研究中心（慕尼黑））
OmniCam: Unified Multimodal Video Generation via Camera Control
Zhejiang University（浙江大学）；Shanghai Jiao Tong University（上海交通大学）；Beijing University of Technology（北京工业大学）；Jiangnan University（江南大学）；University of Science and Technology of China（中国科学技术大学）；Nanjing university（南京大学）
Lund University（隆德大学）；RISE Research Institutes of Sweden（瑞典国家研究院）
Smart cameras are increasingly used in surveillance solutions in public spaces. Contemporary computer vision applications can be used to recognize events that require intervention by emergency services. Smart cameras can be mounted in locations where citizens feel particularly unsafe, e.g., pathways and underpasses with a history of incidents. One promising approach for smart cameras is edge AI, i.e., deploying AI technology on IoT devices. However, implementing resource-demanding technology such as image recognition using deep neural networks (DNN) on constrained devices is a substantial chal…
Camera pose estimation or camera relocalization is the centerpiece in numerous computer vision tasks such as visual odometry, structure from motion (SfM) and SLAM. In this paper we propose a neural network approach with a graph transformer backbone, namely TransCamP, to address the camera relocalization problem. In contrast with prior work where the pose regression is mainly guided by photometric consistency, TransCamP effectively fuses the image features, camera pose information and inter-frame relative camera motions into encoded graph attributes and is trained towards the graph consistency …
For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it. While calibration data is available on some devices through Augmented Reality (AR) frameworks like ARCore and ARKit, for most cameras this information is not available. Therefore, we propose a web based calibration service that not only aggregates calibration data, but also allows calibrating new cameras on-the-fly. We build upon a novel camera calibration framework that enables even novice users to perform a precise camera calibration in about 2 minutes. This…
Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation
S-Lab, Nanyang Technological University（南洋理工大学S-Lab）；SenseTime Research（商汤科技研究院）；University of Michigan（密歇根大学）；Max-Planck Institute for Informatics（马克斯·普朗克信息学研究所）
School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）
Estimating absolute camera orientations is essential for attitude estimation tasks. An established approach is to first carry out visual odometry (VO) or visual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from the camera poses (6 DOF) estimated by VO or V-SLAM. One drawback of this approach, besides the redundancy in estimating full 6 DOF camera poses, is the dependency on estimating a map (3D scene points) jointly with the 6 DOF poses due to the basic constraint on structure-and-motion. To simplify the task of absolute orientation estimation, we formulate the monocular rotatio…
While Deep Neural Network (DNN) models have provided remarkable advances in machine vision capabilities, their high computational complexity and model sizes present a formidable roadblock to deployment in AIoT-based sensing applications. In this paper, we propose a novel paradigm by which peer nodes in a network can collaborate to improve their accuracy on person detection, an exemplar machine vision task. The proposed methodology requires no re-training of the DNNs and incurs minimal processing latency as it extracts scene summaries from the collaborators and injects back into DNNs of the ref…
MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects
Human-Centered-Computing and Extended Reality Lab（以人为本计算与扩展现实实验室）；Klinikum rechts der Isar（右岸诊所）；Orthopedics and Sports Orthopedics（骨科和运动骨科）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；TH Köln（科隆大学）；Computer Graphics Group（计算机图形学组）；Photogrammetry and Remote Sensing Lab（摄影测量与遥感实验室）
University of Washington（华盛顿大学）；University of California Los Angeles（加州大学洛杉矶分校）；Microsoft Research（微软研究院）
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance. Prior research have explored various "end-to-end" models; however these methods still require several preprocessing steps. These additional operations are often non-trivial to implement making replication and deployment difficult and can even have a higher computational budget than the "core" network itself. In this paper, we propose two novel and efficient neural models for camera-based physiological measurement called EfficientPhys that remove the need for face detection, segment…
