Domain-Aware Universal Style Transfer

TL;DR

This paper introduces a unified neural network architecture that adaptively transfers styles across different domains by incorporating a domainness indicator, enabling better quality stylizations in both artistic and photo-realistic styles.

Contribution

The proposed Domain-aware Style Transfer Networks (DSTN) incorporate a novel domainness indicator and domain-aware skip connections for versatile style transfer across domains.

Findings

Outperforms previous methods in qualitative stylization quality.

Effectively transfers styles across artistic and photo-realistic domains.

Produces superior results on proxy metrics for style transfer.

Abstract

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.