  Image cartoonization is recently dominated by generative adversarial networks
(GANs) from the perspective of unsupervised image-to-image translation, in
which an inherent challenge is to precisely capture and sufficiently transfer
characteristic cartoon styles (e.g., clear edges, smooth color shading,
abstract fine structures, etc.). Existing advanced models try to enhance
cartoonization effect by learning to promote edges adversarially, introducing
style transfer loss, or learning to align style from multiple representation
space. This paper demonstrates that more distinct and vivid cartoonization
effect could be easily achieved with only basic adversarial loss. Observing
that cartoon style is more evident in cartoon-texture-salient local image
regions, we build a region-level adversarial learning branch in parallel with
the normal image-level one, which constrains adversarial learning on
cartoon-texture-salient local patches for better perceiving and transferring
cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler
(CTSS) module is proposed to dynamically sample cartoon-texture-salient patches
from training data. With extensive experiments, we demonstrate that texture
saliency adaptive attention in adversarial learning, as a missing ingredient of
related methods in image cartoonization, is of significant importance in
facilitating and enhancing image cartoon stylization, especially for
high-resolution input pictures.
