Automatic Analysis of Human Body Representations in Western Art
This work addresses art historical analyses by providing automated tools to study human pose depictions in paintings, though it is incremental as it builds on existing pose estimation methods.
The paper tackles the problem of analyzing human body representations in Western art by developing a computer vision pipeline that combines OpenPose and DensePose for pose estimation, addresses occlusion and perspective issues, and normalizes poses to Leonardo da Vinci's Vitruvian Man. The result is an approach that improves over skeleton-based analyses and reveals common and uncommon poses used by artists through hierarchical clustering and visualization.
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.