  We explore the importance of spatial contextual information in human pose
estimation. Most state-of-the-art pose networks are trained in a multi-stage
manner and produce several auxiliary predictions for deep supervision. With
this principle, we present two conceptually simple and yet computational
efficient modules, namely Cascade Prediction Fusion (CPF) and Pose Graph Neural
Network (PGNN), to exploit underlying contextual information. Cascade
prediction fusion accumulates prediction maps from previous stages to extract
informative signals. The resulting maps also function as a prior to guide
prediction at following stages. To promote spatial correlation among joints,
our PGNN learns a structured representation of human pose as a graph. Direct
message passing between different joints is enabled and spatial relation is
captured. These two modules require very limited computational complexity.
Experimental results demonstrate that our method consistently outperforms
previous methods on MPII and LSP benchmark.
