Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth

TL;DR

This paper introduces a novel unsupervised makeup transfer method that decouples content and style information based on frequency analysis, avoiding the need for pseudo ground truths and improving transfer quality.

Contribution

It proposes a frequency-based decoupling approach for makeup transfer that operates without pseudo ground truths, enhancing performance and robustness.

Findings

Effective makeup transfer demonstrated through extensive experiments.

Outperforms existing methods in both qualitative and quantitative evaluations.

Eliminates the need for pseudo ground truth generation.

Abstract

The absence of real targets to guide the model training is one of the main problems with the makeup transfer task. Most existing methods tackle this problem by synthesizing pseudo ground truths (PGTs). However, the generated PGTs are often sub-optimal and their imprecision will eventually lead to performance degradation. To alleviate this issue, in this paper, we propose a novel Content-Style Decoupled Makeup Transfer (CSD-MT) method, which works in a purely unsupervised manner and thus eliminates the negative effects of generating PGTs. Specifically, based on the frequency characteristics analysis, we assume that the low-frequency (LF) component of a face image is more associated with its makeup style information, while the high-frequency (HF) component is more related to its content details. This assumption allows CSD-MT to decouple the content and makeup style information in each face image through the frequency decomposition. After that, CSD-MT realizes makeup transfer by maximizing the consistency of these two types of information between the transferred result and input images, respectively. Two newly designed loss functions are also introduced to further improve the transfer performance. Extensive quantitative and qualitative analyses show the effectiveness of our CSD-MT method. Our code is available at https://github.com/Snowfallingplum/CSD-MT.