  Like many computer vision problems, human pose estimation is a challenging
problem in that recognizing a body part requires not only information from
local area but also from areas with large spatial distance. In order to
spatially pass information, large convolutional kernels and deep layers have
been normally used, introducing high computation cost and large parameter
space. Luckily for pose estimation, human body is geometrically structured in
images, enabling modeling of spatial dependency. In this paper, we propose a
spatial shortcut network for pose estimation task, where information is easier
to flow spatially. We evaluate our model with detailed analyses and present its
outstanding performance with smaller structure.
