Automated Deep Photo Style Transfer
This work addresses the challenge of automated, realistic style transfer for photography applications, though it is incremental in automating segmentation.
The paper tackled the problem of transferring artistic style to a content image while preserving photorealism, achieving realistic results when content and style images are sufficiently similar.
Photorealism is a complex concept that cannot easily be formulated mathematically. Deep Photo Style Transfer is an attempt to transfer the style of a reference image to a content image while preserving its photorealism. This is achieved by introducing a constraint that prevents distortions in the content image and by applying the style transfer independently for semantically different parts of the images. In addition, an automated segmentation process is presented that consists of a neural network based segmentation method followed by a semantic grouping step. To further improve the results a measure for image aesthetics is used and elaborated. If the content and the style image are sufficiently similar, the result images look very realistic. With the automation of the image segmentation the pipeline becomes completely independent from any user interaction, which allows for new applications.