Abstract. Recently, style transfer has received a lot of attention. While
much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint:
a style is more than just a single image or an artist, but previous work
is limited to only a single instance of a style or shows no benefit from
more images. Moreover, previous work has relied on a direct comparison
of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can
introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content
loss, which is trained jointly with a deep encoder-decoder network for
real-time, high-resolution stylization of images and videos. We propose a
quantitative measure for evaluating the quality of a stylized image and
also have art historians rank patches from our approach against those
from previous work. These and our qualitative results ranging from small
image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content