Automatic Generation of Video Game Character Images Using Augmented Structure-and-Style Networks

Abstract

We propose a fast, flexible approach to automatically generating two-dimensional character images for video games. We treat the generation of character images as a two-part machine learning problem. The first task is to generate structured images that represent common attributes of characters as images. The second task is to add details to the structured images that appear to fit with some overall theme for each image. For both tasks, we employ generative adversarial network architectures, with modifications to improve their performance for their respective tasks. The resulting As2-GAN approach generates character images that are as realistic as those generated with the DCGAN approach, and are more consistent in quality and structural resemblance to images from the dataset. The As2-GAN approach also provides image creators with more control over images than typical one-step methods, while being able to generate high quality images using small datasets.