  The task of multi-person human pose estimation in natural scenes is quite
challenging. Existing methods include both top-down and bottom-up approaches.
The main advantage of bottom-up methods is its excellent tradeoff between
estimation accuracy and computational cost. We follow this path and aim to
design smaller, faster, and more accurate neural networks for the regression of
keypoints and limb association vectors. These two regression tasks are
naturally dependent on each other. In this work, we propose a dual-path network
specially designed for multi-person human pose estimation, and compare our
performance with the openpose network in aspects of model size, forward speed,
and estimation accuracy.
