Developing Creative AI to Generate Sculptural Objects
Abstract
We explore the intersection of human and machine creativity by generating sculptural objects through machine learning. This research raises questions about both the technical details of automatic art generation and the interaction between AI and people, as both artists and the audience of art. We introduce two algorithms for generating 3D point clouds and then discuss their actualization as sculpture and incorporation into a holistic art installation. Specifically, the Amalgamated DeepDream (ADD) algorithm solves the sparsity problem caused by the naive DeepDream-inspired approach and generates creative and printable point clouds. The Partitioned DeepDream (PDD) algorithm further allows us to explore more diverse 3D object creation by combining point cloud clustering algorithms and ADD.
Cite
@article{arxiv.1908.07587,
title = {Developing Creative AI to Generate Sculptural Objects},
author = {Songwei Ge and Austin Dill and Eunsu Kang and Chun-Liang Li and Lingyao Zhang and Manzil Zaheer and Barnabas Poczos},
journal= {arXiv preprint arXiv:1908.07587},
year = {2019}
}
Comments
In the Proceedings of International Symposium on Electronic Art (ISEA 2019)