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

Authors

  • Kiymet Akdemir Virginia Tech, Blacksburg, VA, USA
  • Jing Shi Adobe Research, San Jose, CA, USA
  • Kushal Kafle Adobe Research, San Jose, CA, USA
  • Brian L. Price Adobe Research, San Jose, CA, USA
  • Pinar Yanardag Virginia Tech, Blacksburg, VA, USA

DOI:

https://doi.org/10.1609/aaai.v40i3.37147

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.

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Published

2026-03-14

How to Cite

Akdemir, K., Shi, J., Kafle, K., Price, B. L., & Yanardag, P. (2026). Plot’n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models. Proceedings of the AAAI Conference on Artificial Intelligence, 40(3), 1694–1702. https://doi.org/10.1609/aaai.v40i3.37147

Issue

Section

AAAI Technical Track on Cognitive Modeling & Cognitive Systems