Abstract

Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another. Traditional layered motion methods, however, employ fairly weak models of scene structure, relying on locally connected Ising/Potts models which have limited ability to capture long-range correlations in natural scenes. To address this, we formulate a fully-connected layered model that enables global reasoning about the complicated segmentations of real objects. Optimization with fully-connected graphical models is challenging, and our inference algorithm leverages recent work on effificient mean fifield updates for fully-connected conditional random fifields. These methods can be implemented effificiently using high-dimensional Gaussian fifiltering. We combine these ideas with a layered flflow model, and fifind that the long-range connections greatly improve segmentation into fifigure-ground layers when compared with locally connected MRF models. Experiments on several benchmark datasets show that the method can recover fifine structures and large occlusion regions, with good flflow accuracy and much lower computational cost than previous locally-connected layered models