Motion Prior

Motion priors are learned representations of movement patterns used to improve the accuracy and efficiency of various computer vision and robotics tasks. Current research focuses on integrating motion priors into diverse model architectures, including diffusion models, transformers, and variational autoencoders, to address challenges like human motion estimation from video, trajectory prediction for autonomous vehicles, and video generation. These advancements are significantly impacting fields such as robotics, autonomous driving, and animation by enabling more realistic and robust systems capable of handling complex, dynamic environments.