  In this paper, we propose efficient and effective methods for 2D human pose
estimation. A new ResBlock is proposed based on depthwise separable convolution
and is utilized instead of the original one in Hourglass network. It can be
further enhanced by replacing the vanilla depthwise convolution with a mixed
depthwise convolution. Based on it, we propose a bottom-up multi-person pose
estimation method. A rooted tree is used to represent human pose by introducing
person centroid as the root which connects to all body joints directly or
hierarchically. Two branches of sub-networks are used to predict the centroids,
body joints and their offsets to their parent nodes. Joints are grouped by
tracing along their offsets to the closest centroids. Experimental results on
the MPII human dataset and the LSP dataset show that both our single-person and
multi-person pose estimation methods can achieve competitive accuracies with
low computational costs.
