​​Domain-Aware Universal Style Transfer Style transfer aims | Data Science by ODS.ai 🦜

​​Domain-Aware Universal Style Transfer

Style transfer aims to reproduce content images with the styles from reference images. Modern style transfer methods can successfully apply arbitrary styles to images in either an artistic or a photo-realistic way. However, due to their structural limitations, they can do it only within a specific domain: the degrees of content preservation and stylization depends on 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.

The authors propose 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. Furthermore, they design a novel domainess indicator (based on the texture and structural features) and 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.

Extensive experiments validate that their model produces better qualitative results and outperforms previous methods in terms of proxy metrics on both artistic and photo-realistic stylizations.

Paper: https://arxiv.org/abs/2108.04441

Code: https://github.com/Kibeom-Hong/Domain-Aware-Style-Transfer

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dstn

#deeplearning #cv #styletransfer