Abstract

In this paper, we propose bridging the gap between StyleGAN3 and human-AI co-creative patterns by augmenting the latent variable model with the ability of image-conditional generation. We modify the existing generator architecture in StyleGAN3, enabling it to use high-level visual ideas to guide the human-AI co-creation. The resulting model, StyleGAN-Canvas, can solve various image-to-image translation tasks while maintaining the internal behaviour of StyleGAN3. We deploy our models to a real-time graphic interface and conduct qualitative human subject studies. We use the mixed initiative generative AI interfaces (MIGAI) framework to frame our findings and present a preliminary evaluation of our models’ usability in a generic co-creative context.