Towards Book Cover Design via Layout Graphs

TL;DR

This paper introduces a neural network-based method for generating customizable book covers from simple layout graphs, making cover design accessible without professional skills.

Contribution

It presents a novel generative model that creates book covers from layout graphs, integrating graph embedding, object proposal, and style transfer techniques.

Findings

Enables easy control over book cover design

Produces unique and high-quality covers

Combines multiple neural network components effectively

Abstract

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.