  This paper is on human pose estimation using Convolutional Neural Networks.
Our main contribution is a CNN cascaded architecture specifically designed for
learning part relationships and spatial context, and robustly inferring pose
even for the case of severe part occlusions. To this end, we propose a
detection-followed-by-regression CNN cascade. The first part of our cascade
outputs part detection heatmaps and the second part performs regression on
these heatmaps. The benefits of the proposed architecture are multi-fold: It
guides the network where to focus in the image and effectively encodes part
constraints and context. More importantly, it can effectively cope with
occlusions because part detection heatmaps for occluded parts provide low
confidence scores which subsequently guide the regression part of our network
to rely on contextual information in order to predict the location of these
parts. Additionally, we show that the proposed cascade is flexible enough to
readily allow the integration of various CNN architectures for both detection
and regression, including recent ones based on residual learning. Finally, we
illustrate that our cascade achieves top performance on the MPII and LSP data
sets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/
