Stylizing Face Images via Multiple Exemplars

TL;DR

This paper introduces a novel method for stylizing face images by leveraging multiple exemplars and Markov Random Fields to improve local style transfer accuracy, resulting in more visually appealing images.

Contribution

It presents a new algorithm that uses multiple exemplars and MRFs for more accurate facial style transfer compared to single-exemplar methods.

Findings

Consistently produces visually pleasing stylized face images.

Outperforms single-exemplar style transfer methods.

Effectively removes artifacts with edge-preserving filtering.

Abstract

We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not contain sufficient stylized facial components for a given photo. In this work, we propose an algorithm to stylize face images using multiple exemplars containing different subjects in the same style. Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks. As image patches from multiple exemplars are used, the boundaries of facial components on the target image are inevitably inconsistent. The artifacts are removed by a post-processing step using an edge-preserving filter. Experimental results show that the proposed algorithm consistently produces visually pleasing results.