  In this work, the human parsing task, namely decomposing a human image into
semantic fashion/body regions, is formulated as an Active Template Regression
(ATR) problem, where the normalized mask of each fashion/body item is expressed
as the linear combination of the learned mask templates, and then morphed to a
more precise mask with the active shape parameters, including position, scale
and visibility of each semantic region. The mask template coefficients and the
active shape parameters together can generate the human parsing results, and
are thus called the structure outputs for human parsing. The deep Convolutional
Neural Network (CNN) is utilized to build the end-to-end relation between the
input human image and the structure outputs for human parsing. More
specifically, the structure outputs are predicted by two separate networks. The
first CNN network is with max-pooling, and designed to predict the template
coefficients for each label mask, while the second CNN network is without
max-pooling to preserve sensitivity to label mask position and accurately
predict the active shape parameters. For a new image, the structure outputs of
the two networks are fused to generate the probability of each label for each
pixel, and super-pixel smoothing is finally used to refine the human parsing
result. Comprehensive evaluations on a large dataset well demonstrate the
significant superiority of the ATR framework over other state-of-the-arts for
human parsing. In particular, the F1-score reaches $64.38\%$ by our ATR
framework, significantly higher than $44.76\%$ based on the state-of-the-art
algorithm.
