  Arbitrary style transfer is the task of synthesis of an image that has never
been seen before, using two given images: content image and style image. The
content image forms the structure, the basic geometric lines and shapes of the
resulting image, while the style image sets the color and texture of the
result. The word "arbitrary" in this context means the absence of any one
pre-learned style. So, for example, convolutional neural networks capable of
transferring a new style only after training or retraining on a new amount of
data are not con-sidered to solve such a problem, while networks based on the
attention mech-anism that are capable of performing such a transformation
without retraining - yes. An original image can be, for example, a photograph,
and a style image can be a painting of a famous artist. The resulting image in
this case will be the scene depicted in the original photograph, made in the
stylie of this picture. Recent arbitrary style transfer algorithms make it
possible to achieve good re-sults in this task, however, in processing portrait
images of people, the result of such algorithms is either unacceptable due to
excessive distortion of facial features, or weakly expressed, not bearing the
characteristic features of a style image. In this paper, we consider an
approach to solving this problem using the combined architecture of deep neural
networks with a attention mechanism that transfers style based on the contents
of a particular image segment: with a clear predominance of style over the form
for the background part of the im-age, and with the prevalence of content over
the form in the image part con-taining directly the image of a person.
