  Instance based photo cartoonization is one of the challenging image
stylization tasks which aim at transforming realistic photos into cartoon style
images while preserving the semantic contents of the photos. State-of-the-art
Deep Neural Networks (DNNs) methods still fail to produce satisfactory results
with input photos in the wild, especially for photos which have high contrast
and full of rich textures. This is due to that: cartoon style images tend to
have smooth color regions and emphasized edges which are contradict to
realistic photos which require clear semantic contents, i.e., textures, shapes
etc. Previous methods have difficulty in satisfying cartoon style textures and
preserving semantic contents at the same time. In this work, we propose a novel
"CartoonRenderer" framework which utilizing a single trained model to generate
multiple cartoon styles. In a nutshell, our method maps photo into a feature
model and renders the feature model back into image space. In particular,
cartoonization is achieved by conducting some transformation manipulation in
the feature space with our proposed Soft-AdaIN. Extensive experimental results
show our method produces higher quality cartoon style images than prior arts,
with accurate semantic content preservation. In addition, due to the decoupling
of whole generating process into "Modeling-Coordinating-Rendering" parts, our
method could easily process higher resolution photos, which is intractable for
existing methods.
