  The way the human body is depicted in classical and modern paintings is
relevant for art historical analyses. Each artist has certain themes and
concerns, resulting in different poses being used more heavily than others. In
this paper, we propose a computer vision pipeline to analyse human pose and
representations in paintings, which can be used for specific artists or
periods. Specifically, we combine two pose estimation approaches (OpenPose and
DensePose, respectively) and introduce methods to deal with occlusion and
perspective issues. For normalisation, we map the detected poses and contours
to Leonardo da Vinci's Vitruvian Man, the classical depiction of body
proportions. We propose a visualisation approach for illustrating the
articulation of joints in a set of paintings. Combined with a hierarchical
clustering of poses, our approach reveals common and uncommon poses used by
artists. Our approach improves over purely skeleton based analyses of human
body in paintings.
