Domain-Aware Universal Style Transfer
This addresses the problem of domain-specific limitations in style transfer for researchers and practitioners in computer vision, offering a more flexible solution, though it is incremental as it builds on existing universal methods.
The paper tackled the limitation of existing universal style transfer methods, which are bounded to specific domains, by proposing a unified architecture that transfers both style and domain properties, achieving better qualitative results and outperforming previous methods on proxy metrics for both artistic and photo-realistic stylizations.
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.