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

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 the creation of comics through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include the integration of machine learning models into the human-AI cooperative comic generation process, the deployment of 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 brings engagement of human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.

Duke Scholars

Author Arnav Jhala Engineering Graduate and Professional Programs