Art Creation with Multi-Conditional StyleGANs
Abstract
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.
Cite
Text
Dobler et al. "Art Creation with Multi-Conditional StyleGANs." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/684Markdown
[Dobler et al. "Art Creation with Multi-Conditional StyleGANs." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/dobler2022ijcai-art/) doi:10.24963/IJCAI.2022/684BibTeX
@inproceedings{dobler2022ijcai-art,
title = {{Art Creation with Multi-Conditional StyleGANs}},
author = {Dobler, Konstantin and Hübscher, Florian and Westphal, Jan and Múnera, Alejandro Sierra and de Melo, Gerard and Krestel, Ralf},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4936-4942},
doi = {10.24963/IJCAI.2022/684},
url = {https://mlanthology.org/ijcai/2022/dobler2022ijcai-art/}
}