Towards Book Cover Design via Layout Graphs
This work addresses the need for accessible book cover design tools, though it appears incremental as it builds on existing generative and graph-based methods.
The authors tackled the problem of generating book covers without professional design skills by proposing a generative neural network that uses layout graphs to produce unique and easily controlled covers.
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.