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Arbitrary style transfer is the task of synthesis of an image that has neverbeen seen before, using two given images: content image and style image. Thecontent image forms the structure, the basic geometric lines and shapes of theresulting image, while the style image sets the color and texture of theresult. The word "arbitrary" in this context means the absence of any onepre-learned style. So, for example, convolutional neural networks capable oftransferring a new style only after training or retraining on a new amount ofdata are not con-sidered to solve such a problem, while networks based on theattention mech-anism that are capable of performing such a transformationwithout 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 inthis case will be the scene depicted in the original photograph, made in thestylie of this picture. Recent arbitrary style transfer algorithms make itpossible to achieve good re-sults in this task, however, in processing portraitimages of people, the result of such algorithms is either unacceptable due toexcessive distortion of facial features, or weakly expressed, not bearing thecharacteristic features of a style image. In this paper, we consider anapproach to solving this problem using the combined architecture of deep neuralnetworks with a attention mechanism that transfers style based on the contentsof a particular image segment: with a clear predominance of style over the formfor the background part of the im-age, and with the prevalence of content overthe form in the image part con-taining directly the image of a person.

Neural arbitrary style transfer for portrait images using the attention mechanism