  Photo-to-caricature translation aims to synthesize the caricature as a
rendered image exaggerating the features through sketching, pencil strokes, or
other artistic drawings. Style rendering and geometry deformation are the most
important aspects in photo-to-caricature translation task. To take both into
consideration, we propose an unsupervised contrastive photo-to-caricature
translation architecture. Considering the intuitive artifacts in the existing
methods, we propose a contrastive style loss for style rendering to enforce the
similarity between the style of rendered photo and the caricature, and
simultaneously enhance its discrepancy to the photos. To obtain an exaggerating
deformation in an unpaired/unsupervised fashion, we propose a Distortion
Prediction Module (DPM) to predict a set of displacements vectors for each
input image while fixing some controlling points, followed by the thin plate
spline interpolation for warping. The model is trained on unpaired photo and
caricature while can offer bidirectional synthesizing via inputting either a
photo or a caricature. Extensive experiments demonstrate that the proposed
model is effective to generate hand-drawn like caricatures compared with
existing competitors.
