Generation of Ideal Makeup Face Images Based on StarGAN-v2 Using Interactive Evolutionary Computation

抄録

In this paper, we propose a novel approach to makeup style transfer including hairstyle on the basis of StarGAN-v2, which is a generative adversarial network for multidomain image translation. The proposed method allows users to generate personalized ideal makeup face images by incorporating interactive evolutionary computation (IEC) into the StarGAN-v2 model. Here, the style codes of face images in StarGAN-v2 are considered chromosomes of individuals in the genetic algorithm, being optimized in the process of IEC. Unlike traditional makeup transfer methods that are limited to a fixed style, our system enables users to actively participate in the style selection process, resulting in more diverse and satisfactory makeup face images, considering the human subjective criteria. The results of computer simulations and their subjective evaluation by users are shown to verify the high performance of the proposed method.