EllipBody: A Light-weight and Part-based Representation for Human Pose and Shape Recovery

Human pose and shape recovery is an important task in computer vision and real-world understanding. Current works are tackled due to the lack of 3D annotations for whole body shapes. We find that part segmentation is a very efficient 2D annotation in 3D human body recovery. It not only indicates the location of each part but also contains 3D information through occlusions from the shape of parts, as indicated in Figure 1. To better utilize 3D information contained in part segmentation, we propose a part-level differentiable renderer which model occlusion between parts explicitly. It enhances the performance in both learning-based and optimization-based methods. To further improve the efficiency of the task, we propose a light-weight body model called EllipBody, which uses ellipsoids to indicate each body part. Together with SMPL, the relationship between forward time, performance and number of faces in body models are analyzed. A small number of faces is chosen for achieving good performance and efficiency at the same time. Extensive experiments show that our methods achieve the state-of-the-art results on Human3.6M and LSP dataset for 3D pose estimation and part segmentation.