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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a novel approach to human pose estimation, consisting of a deep convolutional network for part detection and a higher-level spatial model that is motivated as a graphical model, but actually incorporated into the overall deep network as a particular sub-net that has the plausible interpretation of performing a single round of message passing. The system is trained in three steps. In the first two steps, the deep convolutional part detector and the spatial model are trained individually (the spatial message passing network uses the heat map output of the part detector), while in the third step, the unified network is jointly trained via back propagation. Even though the convolutional part detector alone is already a state-of-the-art system, the spatial model is shown to improve results considerably, with even further improvements gained via the joint training procedure.