Art Creation with Multi-Conditional StyleGANs

• URL: http://arxiv.org/abs/2202.11777v1
• Date: Wed, 23 Feb 2022 20:45:41 GMT
• Title: Art Creation with Multi-Conditional StyleGANs
• Authors: Konstantin Dobler, Florian H\"ubscher, Jan Westphal, Alejandro Sierra-M\'unera, Gerard de Melo, Ralf Krestel
• Abstract summary: A human artist needs a combination of unique skills, understanding, and genuine intention to create artworks that evoke deep feelings and emotions. 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.
• Score: 81.72047414190482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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