Motion Decomposition

Motion decomposition aims to separate complex movements into simpler, interpretable components, facilitating analysis and manipulation of motion data across various domains. Current research focuses on developing efficient algorithms, often leveraging transformer networks or neural implicit functions, to decompose motion in images, videos, and point clouds, addressing challenges like motion blur removal, medical image registration, and 3D scene understanding. These advancements improve accuracy and efficiency in tasks such as object tracking, depth estimation, and human motion synthesis, impacting fields from autonomous driving to medical imaging and animation. The ability to disentangle different motion components enhances the interpretability and usability of motion data for a wide range of applications.