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.

翻译：生成对抗网络（GANs）的最新进展持续吸引着不同领域研究者的关注，其关键特性衍生出的广泛实际应用为其提供了重要支撑。尽管当前多数GAN研究聚焦于生成超逼真图像，但本项工作中的超写实输出并非首要目标。本研究将生成结果作为认知组件，用于在多媒体系列项目的新角色构思过程中提升角色设计师的创造力。为筛选最适合该创意场景的GAN架构，我们首先对比了不同GAN体系结构在单一图形处理器上从头训练新型视觉角色数据集时的性能表现。同时探索了迁移学习与数据增强等替代技术，以突破计算资源限制——这是该领域研究者普遍面临的挑战。此外，采用混合研究方法评估生成图像对角色设计师构思新角色的认知价值。实验结果表明该方案在该场景中具有显著有效性，这从角色设计流程的早期适应性调整中得以印证。作为本研究的延伸，提出的方法将作为人机协同设计的新型范式接受进一步评估，旨在探究生成概念如何及在何处与设计过程产生互动并影响设计产出。