Augmenting Character Designers Creativity Using Generative Adversarial Networks

TL;DR

This paper explores how different GAN architectures can be used to generate visual concepts that enhance character designers' creativity, evaluating their performance and cognitive impact in a multimedia design context.

Contribution

It compares various GAN models for creative character design, incorporating transfer learning and data augmentation to address resource constraints, and assesses their influence on designers' conceptualization process.

Findings

GANs effectively augment character design creativity

Transfer learning reduces training time and resource needs

Generated visuals positively influence designers' conceptualization

Abstract

Recent advances in Generative Adversarial Networks (GANs) continue to attract the attention of researchers in different fields due to the wide range of applications devised to take advantage of their key features. Most recent GANs are focused on realism, however, generating hyper-realistic output is not a priority for some domains, as in the case of this work. The generated outcomes are used here as cognitive components to augment character designers creativity while conceptualizing new characters for different multimedia projects. To select the best-suited GANs for such a creative context, we first present a comparison between different GAN architectures and their performance when trained from scratch on a new visual characters dataset using a single Graphics Processing Unit. We also explore alternative techniques, such as transfer learning and data augmentation, to overcome computational resource limitations, a challenge faced by many researchers in the domain. Additionally, mixed methods are used to evaluate the cognitive value of the generated visuals on character designers agency conceptualizing new characters. The results discussed proved highly effective for this context, as demonstrated by early adaptations to the characters design process. As an extension for this work, the presented approach will be further evaluated as a novel co-design process between humans and machines to investigate where and how the generated concepts are interacting with and influencing the design process outcome.