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
Using Capability Maps Tailored to Arm Range of Motion in VR Exergames for Rehabilitation
Christian Lourido, Zaid Waghoo, Hassam Khan Wazir, Nishtha Bhagat, Vikram Kapila
Characterizing Manipulation Robustness through Energy Margin and Caging Analysis
Yifei Dong, Xianyi Cheng, Florian T. Pokorny
MultiPhys: Multi-Person Physics-aware 3D Motion Estimation
Nicolas Ugrinovic, Boxiao Pan, Georgios Pavlakos, Despoina Paschalidou, Bokui Shen, Jordi Sanchez-Riera, Francesc Moreno-Noguer, Leonidas Guibas
Seeing Motion at Nighttime with an Event Camera
Haoyue Liu, Shihan Peng, Lin Zhu, Yi Chang, Hanyu Zhou, Luxin Yan