Automatic generation of graphic designs has recently received considerable attention. However, the state-of-the-art approaches are complex and rely on proprietary datasets, which creates reproducibility barriers. In this paper, we propose an open framework for automatic graphic design called OpenCOLE, where we build a modified version of the pioneering COLE and train our model exclusively on publicly available datasets. Based on GPT4V evaluations, our model shows promising performance comparable to the original COLE. We release the pipeline and training results to encourage open development.
@article{arxiv.2406.08232,
title = {OpenCOLE: Towards Reproducible Automatic Graphic Design Generation},
author = {Naoto Inoue and Kento Masui and Wataru Shimoda and Kota Yamaguchi},
journal= {arXiv preprint arXiv:2406.08232},
year = {2024}
}
Comments
To appear as an extended abstract (EA) in Workshop on Graphic Design Understanding and Generation (in CVPR2024), code: https://github.com/CyberAgentAILab/OpenCOLE