Autoren:
Dobler, Konstantin
Hübscher, Florian
Westphal, Jan
Sierra-Múnera, Alejandro
de Melo, Gerard
Krestel, Ralf
Zusammenfassung:
Creating art is often viewed as a uniquely human endeavor. 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.
