  Automatic generation of facial images has been well studied after the
Generative Adversarial Network (GAN) came out. There exists some attempts
applying the GAN model to the problem of generating facial images of anime
characters, but none of the existing work gives a promising result. In this
work, we explore the training of GAN models specialized on an anime facial
image dataset. We address the issue from both the data and the model aspect, by
collecting a more clean, well-suited dataset and leverage proper, empirical
application of DRAGAN. With quantitative analysis and case studies we
demonstrate that our efforts lead to a stable and high-quality model. Moreover,
to assist people with anime character design, we build a website
(http://make.girls.moe) with our pre-trained model available online, which
makes the model easily accessible to general public.
