# Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation

arXiv: https://arxiv.org/abs/2101.11111 · HTML: https://mlsanity.com/paper/2101.11111/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [StoryGAN: A Sequential Conditional GAN for Story Visualization](https://mlsanity.com/paper/1812.02784/) (2018)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Neural Storyboard Artist: Visualizing Stories with Coherent Image Sequences](https://mlsanity.com/paper/1911.10460/) (2019)

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

- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [ComicScene154: A Scene Dataset for Comic Analysis](https://mlsanity.com/paper/2508.16190/) (2025)
- [MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation](https://mlsanity.com/paper/2605.28173/) (2026)

## Abstract (arXiv)

In this paper, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approach first extracts informative keyframes by analyzing the subtitles, and stylizes keyframes into comic-style images. Then, we propose a novel automatic multi-page layout framework, which can allocate the images across multiple pages and synthesize visually interesting layouts based on the rich semantics of the images (e.g., importance and inter-image relation). Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audios. Our method is able to vary balloon shapes and word sizes in balloons in response to different emotions, leading to more enriched reading experience. Once the balloons are generated, they are placed adjacent to their corresponding speakers via speaker detection. Our results show that our method, without requiring any user inputs, can generate high-quality comic pages with visually rich layouts and balloons. Our user studies also demonstrate that users prefer our generated results over those by state-of-the-art comic generation systems.