# Towards Book Cover Design via Layout Graphs

arXiv: https://arxiv.org/abs/2105.11088 · HTML: https://mlsanity.com/paper/2105.11088/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [Specifying Object Attributes and Relations in Interactive Scene Generation](https://mlsanity.com/paper/1909.05379/) (2019)
- [READ: Recursive Autoencoders for Document Layout Generation](https://mlsanity.com/paper/1909.00302/) (2019)
- [PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph](https://mlsanity.com/paper/1905.01608/) (2019)
- [GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks](https://mlsanity.com/paper/1905.12502/) (2019)
- [Generating Triples with Adversarial Networks for Scene Graph Construction](https://mlsanity.com/paper/1802.02598/) (2018)
- [How do Convolutional Neural Networks Learn Design?](https://mlsanity.com/paper/1808.08402/) (2018)

## Built on by (most cited first, 3 of 3)

- [Book Cover Synthesis from the Summary](https://mlsanity.com/paper/2211.02138/) (2022)
- [From Fragment to One Piece: A Survey on AI-Driven Graphic Design](https://mlsanity.com/paper/2503.18641/) (2025)
- [Interleaving GANs with knowledge graphs to support design creativity for book covers](https://mlsanity.com/paper/2308.01626/) (2023)

## Abstract (arXiv)

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.