  Caricature, a type of exaggerated artistic portrait, amplifies the
distinctive, yet nuanced traits of human faces. This task is typically left to
artists, as it has proven difficult to capture subjects' unique characteristics
well using automated methods. Recent development of deep end-to-end methods has
achieved promising results in capturing style and higher-level exaggerations.
However, a key part of caricatures, face warping, has remained challenging for
these systems. In this work, we propose AutoToon, the first supervised deep
learning method that yields high-quality warps for the warping component of
caricatures. Completely disentangled from style, it can be paired with any
stylization method to create diverse caricatures. In contrast to prior art, we
leverage an SENet and spatial transformer module and train directly on artist
warping fields, applying losses both prior to and after warping. As shown by
our user studies, we achieve appealing exaggerations that amplify
distinguishing features of the face while preserving facial detail.
