Automatic Content-Aware Color and Tone Stylization

TL;DR

This paper presents an unsupervised method for automatic, diverse, and artifact-free photo stylization by learning style rankings and transferring global color and tone from exemplars.

Contribution

It introduces a novel unsupervised style ranking and a new style transfer technique that avoids common artifacts, improving photo stylization quality.

Findings

Produces visually compelling stylizations

Achieves artifact-free results

Preferred in user studies over existing methods

Abstract

We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also propose a novel technique that transfers the global color and tone of the chosen exemplars to the input photograph while avoiding the common visual artifacts produced by the existing style transfer methods. Together, our style selection and transfer techniques produce compelling, artifact-free results on a wide range of input photographs, and a user study shows that our results are preferred over other techniques.