ComicScene154: A Scene Dataset for Comic Analysis

2025-08-22

AI Summary

Current computational narrative analysis insufficiently addresses comics—a multimodal medium integrating text and images—particularly lacking scene-level narrative arc annotations, thereby hindering advances in multimodal narrative understanding and computational comic analysis. To address this gap, we introduce ComicArc, the first fine-grained, scene-level narrative arc dataset for comics, comprising 154 public-domain comic stories. We propose a text-image cue–guided scene segmentation pipeline, rigorously refined and validated through expert human annotation. ComicArc establishes the first systematic annotation guidelines for scene boundaries and narrative arcs in comics, providing a novel paradigm and benchmark resource for multimodal narrative research. Furthermore, we construct the first comic scene segmentation benchmark, achieving significant improvements in narrative structure recognition performance. This work advances computational comic analysis from page- or panel-level processing toward semantically coherent, scene-level modeling.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• Creating a manually annotated dataset for comic scene analysis
• Developing a baseline scene segmentation pipeline for comics
• Advancing computational methods for multimodal narrative understanding

Innovation

• Manually annotated scene-level narrative arcs dataset
• Baseline scene segmentation pipeline for comics
• Multimodal narrative understanding computational methods