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
Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation
Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, Limin Wang
How to Communicate Robot Motion Intent: A Scoping Review
Max Pascher, Uwe Gruenefeld, Stefan Schneegass, Jens Gerken
Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens
Sen Yang, Wen Heng, Gang Liu, Guozhong Luo, Wankou Yang, Gang Yu