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

Human pose estimation via Convolutional Part Heatmap Regression