  This work proposes to combine neural networks with the compositional
hierarchy of human bodies for efficient and complete human parsing. We
formulate the approach as a neural information fusion framework. Our model
assembles the information from three inference processes over the hierarchy:
direct inference (directly predicting each part of a human body using image
information), bottom-up inference (assembling knowledge from constituent
parts), and top-down inference (leveraging context from parent nodes). The
bottom-up and top-down inferences explicitly model the compositional and
decompositional relations in human bodies, respectively. In addition, the
fusion of multi-source information is conditioned on the inputs, i.e., by
estimating and considering the confidence of the sources. The whole model is
end-to-end differentiable, explicitly modeling information flows and
structures. Our approach is extensively evaluated on four popular datasets,
outperforming the state-of-the-arts in all cases, with a fast processing speed
of 23fps. Our code and results have been released to help ease future research
in this direction.
