Augmenting Character Designers Creativity Using Generative Adversarial
  Networks
Mohammad Lataifeh Xavier Carrasco Ashraf Elnagar Naveed Ahmed
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.