Art Creation with Multi-Conditional StyleGANs

Abstract

In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art. Our approach is based on the StyleGAN neural network architecture, but incorporates a custom multi-conditional control mechanism that provides fine-granular control over characteristics of the generated paintings, e.g., with regard to the perceived emotion evoked in a spectator. We also investigate several evaluation techniques tailored to multi-conditional generation.

Konstantin Dobler

PhD Student & Graduate Researcher

I’m a PhD student at Hasso Plattner Institute researching transfer learning of large language models.