We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs. We demonstrate its potential with a perfume bottle design case study, including human evaluation and quantitative and qualitative analyses.
@article{arxiv.2001.08791,
title = {Machine learning based co-creative design framework},
author = {Brian Quanz and Wei Sun and Ajay Deshpande and Dhruv Shah and Jae-eun Park},
journal= {arXiv preprint arXiv:2001.08791},
year = {2020}
}
Comments
Thirty-third Conference on Neural Information Processing Systems (NeurIPS) 2019 Workshop on Machine Learning for Creativity and Design, December 14th, 2019, Vancouver, Canada (https://neurips2019creativity.github.io/)