  This paper is on highly accurate and highly efficient human pose estimation.
Recent works based on Fully Convolutional Networks (FCNs) have demonstrated
excellent results for this difficult problem. While residual connections within
FCNs have proved to be quintessential for achieving high accuracy, we
re-analyze this design choice in the context of improving both the accuracy and
the efficiency over the state-of-the-art. In particular, we make the following
contributions: (a) We propose gated skip connections with per-channel learnable
parameters to control the data flow for each channel within the module within
the macro-module. (b) We introduce a hybrid network that combines the HourGlass
and U-Net architectures which minimizes the number of identity connections
within the network and increases the performance for the same parameter budget.
Our model achieves state-of-the-art results on the MPII and LSP datasets. In
addition, with a reduction of 3x in model size and complexity, we show no
decrease in performance when compared to the original HourGlass network.
