Motion Learning for Dynamic Scene Understanding

An important goal of computer vision is to automatically understand the visual world. With the introduction of deep networks, we see huge progress in static image understanding. However, we live in a dynamic world, so it is far from enough to merely understand static images. Motion plays a key role in analyzing dynamic scenes and has been one of the fundamental research topics in computer vision. It has wide applications in many fields, including video analysis, socially-aware robotics, autonomous driving, etc. In this dissertation, we study motion from two perspectives: geometric and semantic. From the geometric perspective, we aim to accurately estimate the 3D motion (or scene flow) and 3D structure of the scene. Since manually annotating motion is difficult, we propose self-supervised models for scene flow estimation from image and point cloud sequences. From the semantic perspective, we aim to understand the meanings of different motion patterns and first show that motion benefits detecting and tracking objects from videos. Then we propose a framework to understand the intentions and predict the future locations of agents in a scene. Finally, we study the role of motion information in action recognition