Plot’n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models

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Authors: Association for Artificial Intelligence 2026, Kiymet Akdemir, Kushal Kafle, Brian L. Price, Jing Shi, Pinar Yanardag
Cited by: 0

Abstract

Text-to-image diffusion models have demonstrated significant capabilities to generate diverse and detailed visuals in various domains, and story visualization is emerging as a particularly promising application. However, as their use in real-world creative domains increases, the need for providing enhanced control, refinement, and the ability to modify images post-generation in a consistent manner becomes an important challenge. Existing methods often lack the flexibility to apply fine or coarse edits while maintaining visual and narrative consistency across multiple frames, preventing creators from seamlessly crafting and refining their visual stories. To address these challenges, we introduce Plot'n Polish, a zero-shot framework that enables consistent story generation and provides fine-grained control over story visualizations at various levels of detail.

Fields of Study & Subjects

Generative Adversarial Networks and Image Synthesis Multimodal Machine Learning Applications Digital Media and Visual Art

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN