A method for recovering a part-based description of human pose from single images of people is described. It is able to perform estimation efficiently in the presence significant background clutter, large foreground variation, self-occlusion and occlusion by other objects. This is achieved through two key developments. Firstly, a new formulation is proposed that allows partial configurations, hypotheses with differing numbers of parts, to be made and compared. This permits efficient global sampling in the presence of self and other object occlusions without prior knowledge of body part visibility. Secondly, a highly discriminatory likelihood model is proposed comprising two complementary components. A boundary component improves upon previous appearance distribution divergence methods by incorporating high-level shape and appearance information and hence better discriminates textured, overlapping body parts. An inter-part component uses appearance similarity of body parts to reduce the number of false-positive, multi-part hypotheses, hence increasing estimation efficiency. Results are presented for challenging images with unknown subject and large variations in subject appearance, scale and pose.