  A caricature is an artistic form of a person's picture in which certain
striking characteristics are abstracted or exaggerated in order to create a
humor or sarcasm effect. For numerous caricature related applications such as
attribute recognition and caricature editing, face parsing is an essential
pre-processing step that provides a complete facial structure understanding.
However, current state-of-the-art face parsing methods require large amounts of
labeled data on the pixel-level and such process for caricature is tedious and
labor-intensive. For real photos, there are numerous labeled datasets for face
parsing. Thus, we formulate caricature face parsing as a domain adaptation
problem, where real photos play the role of the source domain, adapting to the
target caricatures. Specifically, we first leverage a spatial transformer based
network to enable shape domain shifts. A feed-forward style transfer network is
then utilized to capture texture-level domain gaps. With these two steps, we
synthesize face caricatures from real photos, and thus we can use parsing
ground truths of the original photos to learn the parsing model. Experimental
results on the synthetic and real caricatures demonstrate the effectiveness of
the proposed domain adaptation algorithm. Code is available at:
https://github.com/ZJULearning/CariFaceParsing .
