  Albeit the recent progress in single image 3D human pose estimation due to
the convolutional neural network, it is still challenging to handle real
scenarios such as highly occluded scenes. In this paper, we propose to address
the problem of single image 3D human pose estimation with occluded measurements
by exploiting the Euclidean distance matrix (EDM). Specifically, we present two
approaches based on EDM, which could effectively handle occluded joints in 2D
images. The first approach is based on 2D-to-2D distance matrix regression
achieved by a simple CNN architecture. The second approach is based on sparse
coding along with a learned over-complete dictionary. Experiments on the
Human3.6M dataset show the excellent performance of these two approaches in
recovering occluded observations and demonstrate the improvements in accuracy
for 3D human pose estimation with occluded joints.
