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 co-design 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 processes 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 co-creation 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.

生成对抗网络（GAN）应用的最新进展继续吸引不同领域研究人员的关注。在这样的框架中，两个神经网络进行逆向竞争，以生成与原始数据集无法区分的新视觉内容。这项研究的目的是在人类和机器之间创建一个互补的协同设计过程，以增强角色设计师为游戏和动画等多媒体项目可视化和创建新角色的能力。在设计认知支架的驱动下，所提出的方法旨在为感知、了解和制造的过程提供信息。机器生成的概念被用作角色设计师构思新角色的启动平台。为此工作开发了包含 22 , 000 个字符的标记数据集，并使用不同的 GAN 进行部署，以评估最适合上下文的数据集，然后对机器输出和人类推导进行混合方法评估。讨论的结果证实了所提出的共同创作框架的价值，并阐明了如何将生成的概念用作认知物质，以多种方式与设计师的能力相互作用，以影响概念化新颖角色的创作过程。