Motion Information
Motion information research focuses on accurately estimating and utilizing movement data from various sources, including videos, sensor data, and medical images, for diverse applications. Current research emphasizes developing robust and efficient algorithms, often employing deep learning models like diffusion models and Siamese networks, to address challenges such as motion blur, occlusions, and limited training data. These advancements are significantly impacting fields like computer vision, robotics, and medical imaging, enabling improved 3D reconstruction, autonomous navigation, and medical image analysis. The development of more accurate and generalized motion models continues to be a key focus.
Papers
Visual Geometry Grounded Deep Structure From Motion
Jianyuan Wang, Nikita Karaev, Christian Rupprecht, David Novotny
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion
Yujie Wei, Shiwei Zhang, Zhiwu Qing, Hangjie Yuan, Zhiheng Liu, Yu Liu, Yingya Zhang, Jingren Zhou, Hongming Shan
Calibration and evaluation of a motion measurement system for PET imaging studies
Junxiang Wang, Ti Wu, Iulian I. Iordachita, Peter Kazanzides
Evaluation of a motion measurement system for PET imaging studies
Junxiang Wang, Ti Wu, Iulian I. Iordachita, Peter Kazanzides
Method for robotic motion compensation during PET imaging of mobile subjects
Junxiang Wang, Iulian I. Iordachita, Peter Kazanzides
Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
Daniel Kienzle, Julian Lorenz, Katja Ludwig, Rainer Lienhart
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition
Facundo Manuel Quiroga, Franco Ronchetti, Laura Lanzarini, Cesar Eestrebou