Makeup transfer model based on BeautyGAN

Abstract

Facial makeup transfer can realize automatic application of any makeup styles on the target face without the change of face identity. BeautyGAN enables unsupervised makeup transfer, but there are several problems with generated images, that is, partial loss of makeup effect, poor performance in makeup transfer while the input images or backgrounds are complex, and difficulty in transferring low-resolution images directly. To solve these problems, BeautyGAN, an existing makeup transfer model, was optimized. Referring to the fast style transfer algorithm, a BeautyGAN-based makeup transfer model was designed and developed by introducing a perceptual loss model to improve the performance of BeautyGAN in extracting facial features. The input image is preprocessed by SRGAN network to adapt low-resolution images to BeautyGAN model. The results show that the optimized BeautyGAN has improved local migration performance and can be put into real time operation during testing. Compared with BeautyGAN, the effect of makeup transfer has been significantly improved on the input images with facial expressions, facial occlusion or small angle pose. It is also compatible with low-resolution images.