# ComicScene154: A Scene Dataset for Comic Analysis

arXiv: https://arxiv.org/abs/2508.16190 · HTML: https://mlsanity.com/paper/2508.16190/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [A Survey on Semantic Parsing](https://mlsanity.com/paper/1812.00978/) (2018)
- [A Local-to-Global Approach to Multi-modal Movie Scene Segmentation](https://mlsanity.com/paper/2004.02678/) (2020)
- [The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives](https://mlsanity.com/paper/1611.05118/) (2016)
- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding](https://mlsanity.com/paper/2407.03550/) (2024)
- [Object Detection for Comics using Manga109 Annotations](https://mlsanity.com/paper/1803.08670/) (2018)
- [Zero-Shot Character Identification and Speaker Prediction in Comics via Iterative Multimodal Fusion](https://mlsanity.com/paper/2404.13993/) (2024)
- [ComicGAN: Text-to-Comic Generative Adversarial Network](https://mlsanity.com/paper/2109.09120/) (2021)
- [Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation](https://mlsanity.com/paper/2101.11111/) (2021)
- [Toward accessible comics for blind and low vision readers](https://mlsanity.com/paper/2407.08248/) (2024)

## Built on by (most cited first, 1 of 1)

- [Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges](https://mlsanity.com/paper/2607.19011/) (2026)

## Abstract (arXiv)

Comics offer a compelling yet under-explored domain for computational narrative analysis, combining text and imagery in ways distinct from purely textual or audiovisual media. We introduce ComicScene154, a manually annotated dataset of scene-level narrative arcs derived from public-domain comic books spanning diverse genres. By conceptualizing comics as an abstraction for narrative-driven, multimodal data, we highlight their potential to inform broader research on multi-modal storytelling. To demonstrate the utility of ComicScene154, we present a baseline scene segmentation pipeline, providing an initial benchmark that future studies can build upon. Our results indicate that ComicScene154 constitutes a valuable resource for advancing computational methods in multimodal narrative understanding and expanding the scope of comic analysis within the Natural Language Processing community.