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

2024-12-30

AI Summary

To address challenges in dialog-to-dynamic-multi-view storyboard generation—including information loss, shallow scene understanding, and difficulty modeling cinematic rules—this paper introduces the novel task of “dialog visualization” and proposes the first training-free, multimodal end-to-end framework. Our method adopts a decoupled three-stage architecture—Script Director, Cinematographer, and Storyboard Maker—integrating chain-of-thought reasoning (CoT), retrieval-augmented generation (RAG), and multi-view image synthesis to jointly model linguistic semantics, physical spatial constraints, and cinematic grammar. Leveraging large language models and diffusion models, it achieves cross-modal alignment and director-intent-controllable generation. Experiments demonstrate significant improvements over state-of-the-art methods in script comprehension, physical scene inference, and cinematic rule adherence, yielding substantial gains in storyboard quality, semantic consistency, and creative controllability.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Dialogue-rich Script Conversion
• Visual Storyboarding
• Cinematic Rule Complexity

Innovation

• Dialogue Director
• Storyboard Generation
• Visual Narration Enhancement