Generating Visually Consistent Images for Storytelling via Narrative Graph Prompting

Andrew Shin, Kunitake Kaneko; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 3831-3836

While generative AI has accomplished significant advances in visual qualities, generating coherent visual narratives over timeline still remains challenging due to inconsistencies in recurring elements, such as characters and settings, across different frames or scenes. This paper introduces Narrative Graph Prompting, a novel strategy that employs a structured graph representation to enforce visual consistency in AI-generated stories. Our approach defines canonical visual attributes and unique consistency identifiers for key elements, such as characters, settings, and objects, within a central narrative graph. An orchestrating Large Language Model (LLM) then leverages this graph to generate highly detailed, scene-specific prompts for visual generation models, critically injecting these consistent identifiers. This method ensures that visual models render elements with maintained appearance throughout the narrative, reducing the occurrences of visual drift. We demonstrate how this structured approach enhances visual consistency, provides a framework for scalable narrative generation, and offers fine-grained control over story elements, paving the way for more compelling and coherent AI-powered visual storytelling.