Style transfer aims to reproduce content images with the styles from
reference images. Existing universal style transfer methods successfully
deliver arbitrary styles to original images either in an artistic or a
photo-realistic way. However, the range of 'arbitrary style' defined by
existing works is bounded in the particular domain due to their structural
limitation. Specifically, the degrees of content preservation and stylization
are established according to a predefined target domain. As a result, both
photo-realistic and artistic models have difficulty in performing the desired
style transfer for the other domain. To overcome this limitation, we propose a
unified architecture, Domain-aware Style Transfer Networks (DSTN) that transfer
not only the style but also the property of domain (i.e., domainness) from a
given reference image. To this end, we design a novel domainness indicator that
captures the domainness value from the texture and structural features of
reference images. Moreover, we introduce a unified framework with domain-aware
skip connection to adaptively transfer the stroke and palette to the input
contents guided by the domainness indicator. Our extensive experiments validate
that our model produces better qualitative results and outperforms previous
methods in terms of proxy metrics on both artistic and photo-realistic
stylizations.

该研究提出了一种新的基于领域感知的样式迁移网络（DSTN），旨在通过捕捉参考图像的领域性质来传递样式。实验结果表明，该模型在艺术和照片逼真的渲染中均比先前的方法表现更好。