  We propose a novel efficient and lightweight model for human pose estimation
from a single image. Our model is designed to achieve competitive results at a
fraction of the number of parameters and computational cost of various
state-of-the-art methods. To this end, we explicitly incorporate part-based
structural and geometric priors in a hierarchical prediction framework. At the
coarsest resolution, and in a manner similar to classical part-based
approaches, we leverage the kinematic structure of the human body to propagate
convolutional feature updates between the keypoints or body parts. Unlike
classical approaches, we adopt end-to-end training to learn this geometric
prior through feature updates from data. We then propagate the feature
representation at the coarsest resolution up the hierarchy to refine the
predicted pose in a coarse-to-fine fashion. The final network effectively
models the geometric prior and intuition within a lightweight deep neural
network, yielding state-of-the-art results for a model of this size on two
standard datasets, Leeds Sports Pose and MPII Human Pose.
