  Articulated human pose estimation is a fundamental yet challenging task in
computer vision. The difficulty is particularly pronounced in scale variations
of human body parts when camera view changes or severe foreshortening happens.
Although pyramid methods are widely used to handle scale changes at inference
time, learning feature pyramids in deep convolutional neural networks (DCNNs)
is still not well explored. In this work, we design a Pyramid Residual Module
(PRMs) to enhance the invariance in scales of DCNNs. Given input features, the
PRMs learn convolutional filters on various scales of input features, which are
obtained with different subsampling ratios in a multi-branch network. Moreover,
we observe that it is inappropriate to adopt existing methods to initialize the
weights of multi-branch networks, which achieve superior performance than plain
networks in many tasks recently. Therefore, we provide theoretic derivation to
extend the current weight initialization scheme to multi-branch network
structures. We investigate our method on two standard benchmarks for human pose
estimation. Our approach obtains state-of-the-art results on both benchmarks.
Code is available at https://github.com/bearpaw/PyraNet.
