Flow Trajectory

Flow trajectory research focuses on modeling and manipulating the paths of data points through a continuous transformation, aiming for efficient and high-quality data generation, manipulation, and analysis. Current efforts concentrate on developing novel flow architectures, such as those based on ordinary differential equations and normalizing flows, often incorporating techniques like iterative closest point algorithms and classifier-free guidance to improve accuracy and speed. These advancements have significant implications for diverse fields, including image generation, autonomous driving (via LiDAR scene flow estimation), and Bayesian inverse problems, enabling faster and more accurate solutions in real-time applications.