Style-Transfer via Texture-Synthesis

TL;DR

This paper introduces a novel texture-synthesis based style-transfer algorithm that improves quality and flexibility, producing visually pleasing, diverse images comparable to CNN-based methods, while maintaining content and style consistency.

Contribution

The authors extend Kwatra's texture-synthesis algorithm to enhance style-transfer quality, achieving results comparable to CNN-based approaches with increased speed and flexibility.

Findings

Results are visually pleasing and diverse.

Algorithm is fast and flexible for any content-style pair.

Competitive with recent CNN style-transfer methods.

Abstract

Style-transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image which is an artistic mixture of the two. Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path towards handling the style-transfer task, via generalization of texture-synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared to the CNN ones. In this work we propose a novel style-transfer algorithm that extends the texture-synthesis work of Kwatra et. al. (2005), while aiming to get stylized images that get closer in quality to the CNN ones. We modify Kwatra's algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style-transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.