  Arbitrary style transfer aims to synthesize a content image with the style of
an image to create a third image that has never been seen before. Recent
arbitrary style transfer algorithms find it challenging to balance the content
structure and the style patterns. Moreover, simultaneously maintaining the
global and local style patterns is difficult due to the patch-based mechanism.
In this paper, we introduce a novel style-attentional network (SANet) that
efficiently and flexibly integrates the local style patterns according to the
semantic spatial distribution of the content image. A new identity loss
function and multi-level feature embeddings enable our SANet and decoder to
preserve the content structure as much as possible while enriching the style
patterns. Experimental results demonstrate that our algorithm synthesizes
stylized images in real-time that are higher in quality than those produced by
the state-of-the-art algorithms.
