  We propose a new approach for synthesizing fully detailed art-stylized images
from sketches. Given a sketch, with no semantic tagging, and a reference image
of a specific style, the model can synthesize meaningful details with colors
and textures. The model consists of three modules designed explicitly for
better artistic style capturing and generation. Based on a GAN framework, a
dual-masked mechanism is introduced to enforce the content constraints (from
the sketch), and a feature-map transformation technique is developed to
strengthen the style consistency (to the reference image). Finally, an inverse
procedure of instance-normalization is proposed to disentangle the style and
content information, therefore yields better synthesis performance. Experiments
demonstrate a significant qualitative and quantitative boost over baselines
based on previous state-of-the-art techniques, adopted for the proposed
process.
