  Style transfer has recently received a lot of attention, since it allows to
study fundamental challenges in image understanding and synthesis. Recent work
has significantly improved the representation of color and texture and
computational speed and image resolution. The explicit transformation of image
content has, however, been mostly neglected: while artistic style affects
formal characteristics of an image, such as color, shape or texture, it also
deforms, adds or removes content details. This paper explicitly focuses on a
content-and style-aware stylization of a content image. Therefore, we introduce
a content transformation module between the encoder and decoder. Moreover, we
utilize similar content appearing in photographs and style samples to learn how
style alters content details and we generalize this to other class details.
Additionally, this work presents a novel normalization layer critical for high
resolution image synthesis. The robustness and speed of our model enables a
video stylization in real-time and high definition. We perform extensive
qualitative and quantitative evaluations to demonstrate the validity of our
approach.
