# Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity

arXiv: https://arxiv.org/abs/2409.17263 · HTML: https://mlsanity.com/paper/2409.17263/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation](https://mlsanity.com/paper/2405.01434/) (2024)
- [A Customizable Generator for Comic-Style Visual Narrative](https://mlsanity.com/paper/2401.02863/) (2023)

## Built on by (most cited first, 6 of 6)

- [GameTileNet: A Semantic Dataset for Low-Resolution Game Art in Procedural Content Generation](https://mlsanity.com/paper/2507.02941/) (2025)
- [Narrative-to-Scene Generation: An LLM-Driven Pipeline for 2D Game Environments](https://mlsanity.com/paper/2509.04481/) (2025)
- [Robust Symbolic Reasoning for Visual Narratives via Hierarchical and Semantically Normalized Knowledge Graphs](https://mlsanity.com/paper/2508.14941/) (2025)
- [MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation](https://mlsanity.com/paper/2605.28173/) (2026)
- [Investigating Social Bias in Narrative Image Generation](https://mlsanity.com/paper/2608.01780/) (2026)
- [Reconstructing Persistent Worlds from Narratives for Narrative-Grounded Interactive Experiences](https://mlsanity.com/paper/2608.04037/) (2026)

## Abstract (arXiv)

This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process. Our system combines human creativity with AI models to support parts of the generative process, providing a collaborative platform for creating comic content. These comic-authoring idioms, derived from prior human-created image sequences, serve as guidelines for crafting and refining storytelling. The system translates these principles into system layers that facilitate comic creation through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. This approach improves narrative elements in generated image sequences and engages human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.