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Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling

Min Zhang, Zilin Wang, Liyan Chen, Kunhong Liu, Juncong Lin

TL;DR

Dialogue Director tackles the challenge of translating dialogue-centric scripts into coherent, cinema-grade storyboards by introducing a training-free, three-agent pipeline that fuses language reasoning with diffusion-based visual generation. The Script Director performs structured extraction from scripts using Chain-of-Thought prompting and Retrieval-Augmented Generation, the Cinematographer generates consistent multi-view character visuals, and the Storyboard Maker composes cinematic layouts that respect perspective and shot design. Across real-world scripts, the framework achieves superior image quality and text–image alignment (as shown by NIQE and CLIP-T) and strong human judgments on relationship, physical consistency, and cinematic knowledge, outperforming several state-of-the-art baselines. The approach is flexible and plug-and-play, enabling controllable, dialogue-driven storyboard production with improved narrative coherence and visual fidelity, while noting limitations in highly dynamic shots and complex poses for future work.

Abstract

Recent advances in AI-driven storytelling have enhanced video generation and story visualization. However, translating dialogue-centric scripts into coherent storyboards remains a significant challenge due to limited script detail, inadequate physical context understanding, and the complexity of integrating cinematic principles. To address these challenges, we propose Dialogue Visualization, a novel task that transforms dialogue scripts into dynamic, multi-view storyboards. We introduce Dialogue Director, a training-free multimodal framework comprising a Script Director, Cinematographer, and Storyboard Maker. This framework leverages large multimodal models and diffusion-based architectures, employing techniques such as Chain-of-Thought reasoning, Retrieval-Augmented Generation, and multi-view synthesis to improve script understanding, physical context comprehension, and cinematic knowledge integration. Experimental results demonstrate that Dialogue Director outperforms state-of-the-art methods in script interpretation, physical world understanding, and cinematic principle application, significantly advancing the quality and controllability of dialogue-based story visualization.

Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling

TL;DR

Dialogue Director tackles the challenge of translating dialogue-centric scripts into coherent, cinema-grade storyboards by introducing a training-free, three-agent pipeline that fuses language reasoning with diffusion-based visual generation. The Script Director performs structured extraction from scripts using Chain-of-Thought prompting and Retrieval-Augmented Generation, the Cinematographer generates consistent multi-view character visuals, and the Storyboard Maker composes cinematic layouts that respect perspective and shot design. Across real-world scripts, the framework achieves superior image quality and text–image alignment (as shown by NIQE and CLIP-T) and strong human judgments on relationship, physical consistency, and cinematic knowledge, outperforming several state-of-the-art baselines. The approach is flexible and plug-and-play, enabling controllable, dialogue-driven storyboard production with improved narrative coherence and visual fidelity, while noting limitations in highly dynamic shots and complex poses for future work.

Abstract

Recent advances in AI-driven storytelling have enhanced video generation and story visualization. However, translating dialogue-centric scripts into coherent storyboards remains a significant challenge due to limited script detail, inadequate physical context understanding, and the complexity of integrating cinematic principles. To address these challenges, we propose Dialogue Visualization, a novel task that transforms dialogue scripts into dynamic, multi-view storyboards. We introduce Dialogue Director, a training-free multimodal framework comprising a Script Director, Cinematographer, and Storyboard Maker. This framework leverages large multimodal models and diffusion-based architectures, employing techniques such as Chain-of-Thought reasoning, Retrieval-Augmented Generation, and multi-view synthesis to improve script understanding, physical context comprehension, and cinematic knowledge integration. Experimental results demonstrate that Dialogue Director outperforms state-of-the-art methods in script interpretation, physical world understanding, and cinematic principle application, significantly advancing the quality and controllability of dialogue-based story visualization.
Paper Structure (16 sections, 7 equations, 4 figures, 3 tables)

This paper contains 16 sections, 7 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diffusion Models Leveraging Large Language Models
  4. Story Visualization
  5. Methods
  6. Overall
  7. Script Director
  8. Cinematographer
  9. Storyboard Maker
  10. Experiments
  11. Experiment settings
  12. Quantitative Comparisons
  13. Qualitative Comparisons
  14. Human evaluation
  15. Ablation Analysis
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Motivation of Dialogue Visualization and our framework. Traditional story visualization methods meet with challenges when handling dialogues, while Dialogues play a significant role in storytelling. Existing methods may be good at ID-preserving, but rely on extra manual effort, perform not well in details preserving, character’s orientation, and cinematic knowledge.
  • Figure 2: Pipeline of our dialogue visualization system, Dialogue Director, consists of three components leveraging LLM for human-like textual understanding. (a) Story Director: Bridges the gap between the dialogue script and detailed scene descriptions for generative models by extracting and enriching key elements. (b) Cinematographer: Visualizes characters and scenes, generating multi-view portraits based on the Story Director's instructions. (c) Storyboard Maker: Combines cinematic knowledge with the dialogue script to plan layouts, select portraits, and compose visual elements into the final storyboard.
  • Figure 3: In-the-wild scripts Dialogue Visualization evaluation. Methods in yellow frame mean the use of script only, without any manual effort; methods in green frame mean use textual information with manual effort; methods in blue frame mean the use of reference images generated by the agent cinematographer.
  • Figure 4: Ablation analysis on our method and OmniGen. It can be seen the agents in our method can act as plug-and-play component in other generative method like OmniGen. The three components perform their duties as we expect.