Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling
This addresses the problem of generating dynamic storyboards from dialogue scripts for storytellers and content creators, representing a domain-specific advancement in AI-driven storytelling.
The paper tackles the challenge of translating dialogue-centric scripts into coherent storyboards for multimodal storytelling by proposing Dialogue Visualization, a novel task, and introducing Dialogue Director, a training-free multimodal framework that outperforms state-of-the-art methods in script interpretation, physical world understanding, and cinematic principle application.
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.