Semi-supervised reference-based sketch extraction using a contrastive learning framework

TL;DR

This paper introduces a semi-supervised, contrastive learning framework for style-specific sketch extraction from images, capable of mimicking reference sketch styles without paired datasets, outperforming existing methods.

Contribution

It presents a novel multi-modal sketch extraction approach that effectively imitates reference sketch styles using unpaired data, overcoming limitations of previous methods.

Findings

Outperforms state-of-the-art sketch extraction methods

Achieves higher quality results in style imitation

Effective training with unpaired datasets

Abstract

Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.