# Style Transfer by Relaxed Optimal Transport and Self-Similarity

## TL;DR

STROTSS introduces a novel style transfer method using relaxed optimal transport and self-similarity, enabling user control and achieving higher quality stylization compared to previous approaches.

## Contribution

The paper presents a new optimization-based style transfer algorithm that incorporates user-guided control and outperforms prior methods in style-content tradeoff quality.

## Key findings

- Higher quality stylization at any content preservation level
- Effective user control over style transfer regions
- Superior performance in large-scale user study

## Abstract

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