Style Transfer by Relaxed Optimal Transport and Self-Similarity
This addresses style transfer for image rendering, offering incremental improvements with user guidance to correct errors or achieve specific effects.
The paper tackles style transfer by proposing STROTSS, an optimization-based algorithm that allows user control over visual similarity between style and output, and a user study shows it provides higher quality stylization than prior work for any level of content preservation.
Style transfer algorithms strive to render the content of one image using the style of another. We propose Style Transfer by Relaxed Optimal Transport and Self-Similarity (STROTSS), a new optimization-based style transfer algorithm. We extend our method to allow user-specified point-to-point or region-to-region control over visual similarity between the style image and the output. Such guidance can be used to either achieve a particular visual effect or correct errors made by unconstrained style transfer. In order to quantitatively compare our method to prior work, we conduct a large-scale user study designed to assess the style-content tradeoff across settings in style transfer algorithms. Our results indicate that for any desired level of content preservation, our method provides higher quality stylization than prior work. Code is available at https://github.com/nkolkin13/STROTSS