Art Creation with Multi-Conditional StyleGANs
This work addresses the challenge of automated art creation for applications in digital media and entertainment, though it is incremental as it builds on existing StyleGAN architecture.
The paper tackles the problem of generating realistic paintings that emulate human art by introducing a multi-conditional StyleGAN approach with fine-grained control over characteristics like perceived emotion, resulting in synthesized paintings that evoke deep feelings.
Creating meaningful art is often viewed as a uniquely human endeavor. A human artist needs a combination of unique skills, understanding, and genuine intention to create artworks that evoke deep feelings and emotions. 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. For better control, we introduce the conditional truncation trick, which adapts the standard truncation trick for the conditional setting and diverse datasets. Finally, we develop a diverse set of evaluation techniques tailored to multi-conditional generation.