IJCAI 2022poster12 citations
Art Creation with Multi-Conditional StyleGANs
Konstantin Dobler, Florian Hübscher, Jan Westphal, Alejandro Sierra-Múnera, Gerard de Melo, Ralf Krestel
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.
Application domains: Images and visual artsMethods and resources: Machine learning, deep learning, neural models, reinforcement learning
BibTeX
@inproceedings{ijcai2022p684,
title = {Art Creation with Multi-Conditional StyleGANs},
author = {Dobler, Konstantin and Hübscher, Florian and Westphal, Jan and Sierra-Múnera, Alejandro and de Melo, Gerard and Krestel, Ralf},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {4936--4942},
year = {2022},
month = {7},
note = {AI and Arts},
doi = {10.24963/ijcai.2022/684},
url = {https://doi.org/10.24963/ijcai.2022/684},
}