  Artistic style transfer aims to create new artistic images by rendering a
given photograph with the target artistic style. Existing methods learn styles
simply based on global statistics or local patches, lacking careful
consideration of the drawing process in practice. Consequently, the stylization
results either fail to capture abundant and diversified local style patterns,
or contain undesired semantic information of the style image and deviate from
the global style distribution. To address this issue, we imitate the drawing
process of humans and propose a Two-Stage Statistics-Aware Transformation
(TSSAT) module, which first builds the global style foundation by aligning the
global statistics of content and style features and then further enriches local
style details by swapping the local statistics (instead of local features) in a
patch-wise manner, significantly improving the stylization effects. Moreover,
to further enhance both content and style representations, we introduce two
novel losses: an attention-based content loss and a patch-based style loss,
where the former enables better content preservation by enforcing the semantic
relation in the content image to be retained during stylization, and the latter
focuses on increasing the local style similarity between the style and stylized
images. Extensive qualitative and quantitative experiments verify the
effectiveness of our method.
