Book covers are intentionally designed and provide an introduction to a book. However, they typically require professional skills to design and produce the cover images. Thus, we propose a generative neural network that can produce book covers based on an easy-to-use layout graph. The layout graph contains objects such as text, natural scene objects, and solid color spaces. This layout graph is embedded using a graph convolutional neural network and then used with a mask proposal generator and a bounding-box generator and filled using an object proposal generator. Next, the objects are compiled into a single image and the entire network is trained using a combination of adversarial training, perceptual training, and reconstruction. Finally, a Style Retention Network (SRNet) is used to transfer the learned font style onto the desired text. Using the proposed method allows for easily controlled and unique book covers.
@article{arxiv.2105.11088,
title = {Towards Book Cover Design via Layout Graphs},
author = {Wensheng Zhang and Yan Zheng and Taiga Miyazono and Seiichi Uchida and Brian Kenji Iwana},
journal= {arXiv preprint arXiv:2105.11088},
year = {2021}
}