The goal of SEED is to fundamentally advance the methodology of computer vision by exploiting a dynamic analysis perspective in order to acquire accurate, yet tractable models, that can automatically learn to sense our visual world, localize still and animate objects (e.g. chairs, phones, computers, bicycles or cars, people and animals), actions and interactions, as well as qualitative geometrical ...