  Human pose estimation using deep neural networks aims to map input images
with large variations into multiple body keypoints which must satisfy a set of
geometric constraints and inter-dependency imposed by the human body model.
This is a very challenging nonlinear manifold learning process in a very high
dimensional feature space. We believe that the deep neural network, which is
inherently an algebraic computation system, is not the most effecient way to
capture highly sophisticated human knowledge, for example those highly coupled
geometric characteristics and interdependence between keypoints in human poses.
In this work, we propose to explore how external knowledge can be effectively
represented and injected into the deep neural networks to guide its training
process using learned projections that impose proper prior. Specifically, we
use the stacked hourglass design and inception-resnet module to construct a
fractal network to regress human pose images into heatmaps with no explicit
graphical modeling. We encode external knowledge with visual features which are
able to characterize the constraints of human body models and evaluate the
fitness of intermediate network output. We then inject these external features
into the neural network using a projection matrix learned using an auxiliary
cost function. The effectiveness of the proposed inception-resnet module and
the benefit in guided learning with knowledge projection is evaluated on two
widely used benchmarks. Our approach achieves state-of-the-art performance on
both datasets.
