Abstract

This paper presents an approach to parsing humans when there is signifificant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior that, even in presence of occlusion, the visible nodes form a connected subtree of the graphical model. We call each connected subtree a flflexible composition of object parts. This involves a novel method for learning occlusion cues. During inference we need to search over a mixture of different flflexible models. By exploiting part sharing, we show that this inference can be done extremely effificiently requiring only twice as many computations as searching for the entire object (i.e., not modeling occlusion). We evaluate our model on the standard benchmarked “We Are Family” Stickmen dataset and obtain signifificant performance improvements over the best alternative algorithms.