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
Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motions
Emmanuel Hartman, Nicolas Charon, Martin Bauer
MA^2: A Self-Supervised and Motion Augmenting Autoencoder for Gait-Based Automatic Disease Detection
Yiqun Liu, Ke Zhang, Yin Zhu
CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection
Jisong Kim, Minjae Seong, Jun Won Choi