Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
This work addresses the need for accessible anime character design tools for the general public, though it is incremental as it builds on existing GAN methods.
The paper tackles the problem of generating high-quality anime facial images by training a specialized GAN model on a clean dataset and applying DRAGAN, resulting in a stable and high-quality model demonstrated through quantitative analysis and case studies.
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.