  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
