  Caricature generation aims to translate real photos into caricatures with
artistic styles and shape exaggerations while maintaining the identity of the
subject. Different from the generic image-to-image translation, drawing a
caricature automatically is a more challenging task due to the existence of
various spacial deformations. Previous caricature generation methods are
obsessed with predicting definite image warping from a given photo while
ignoring the intrinsic representation and distribution for exaggerations in
caricatures. This limits their ability on diverse exaggeration generation. In
this paper, we generalize the caricature generation problem from instance-level
warping prediction to distribution-level deformation modeling. Based on this
assumption, we present the first exploration for unpaired CARIcature generation
with Multiple Exaggerations (CariMe). Technically, we propose a
Multi-exaggeration Warper network to learn the distribution-level mapping from
photo to facial exaggerations. This makes it possible to generate diverse and
reasonable exaggerations from randomly sampled warp codes given one input
photo. To better represent the facial exaggeration and produce fine-grained
warping, a deformation-field-based warping method is also proposed, which helps
us to capture more detailed exaggerations than other point-based warping
methods. Experiments and two perceptual studies prove the superiority of our
method comparing with other state-of-the-art methods, showing the improvement
of our work on caricature generation.
