Recent advances in Generative Adversarial Networks GANs applications continue
to attract the attention of researchers in different fields. In such a
framework, two neural networks compete adversely to generate new visual
contents indistinguishable from the original dataset. The objective of this
research is to create a complementary codesign process between humans and
machines to augment character designers abilities in visualizing and creating
new characters for multimedia projects such as games and animation. Driven by
design cognitive scaffolding, the proposed approach aims to inform the process
of perceiving, knowing, and making. The machine generated concepts are used as
a launching platform for character designers to conceptualize new characters. A
labelled dataset of 22,000 characters was developed for this work and deployed
using different GANs to evaluate the most suited for the context, followed by
mixed methods evaluation for the machine output and human derivations. The
discussed results substantiate the value of the proposed cocreation framework
and elucidate how the generated concepts are used as cognitive substances that
interact with designers competencies in a versatile manner to influence the
creative processes of conceptualizing novel characters.

通过生成对抗网络和角色设计相结合的协同创作框架，本研究开发了一个标记数据集并使用不同的生成对抗网络进行评估，证实了该框架的价值，并阐明了生成的概念如何以多样的方式与设计师的能力相互作用，影响构思新角色的创造过程。