Progressive Generative Adversarial Network Based on Spatial Style for Makeup Transfer

ABSTRACT In recent years, with the rapid advancement of information and communications technology, consumer demands in the beauty and fashion industry have become increasingly diverse and personalised. To address real‐world application challenges, makeup transfer models must be capable of adapting to variations in head poses to ensure accurate makeup transfer across different angles and poses. In response to these challenges, we propose a new progressive makeup transfer model based on spatial style features and feature maps, termed progressive makeup transfer based on style features and feature maps (PMT‐SM). The PMT‐SM model is built upon a vision transformer (ViT) backbone and integrates several key components: feature pyramid network (FPN), makeup feature localiser (MFL), makeup style extractor (MSE) and progressive makeup generator (PMGen). This framework is designed to effectively handle large variations in head poses while ensuring precise and adaptable makeup transfer for real‐world applications. According to the experimental results, PMT‐SM model achieves scores of 0.94, 0.95 and 7.93 in ArcFace similarity, structural similarity index (SSIM) and Fréchet inception distance (FID), respectively, demonstrating superior ability to preserve identity and background information compared with existing makeup transfer models. In addition, the images generated by the proposed model achieve a best‐selected ratio (BSR) of 25.00%, indicating that the PMT‐SM model provides significant advantages in terms of visual quality and naturalness over other makeup transfer methods in the user study.