Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling
- URL: http://arxiv.org/abs/2412.20725v1
- Date: Mon, 30 Dec 2024 05:54:23 GMT
- Title: Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling
- Authors: Min Zhang, Zilin Wang, Liyan Chen, Kunhong Liu, Juncong Lin,
- Abstract summary: 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.
Experimental results demonstrate that Dialogue Director outperforms state-of-the-art methods in script interpretation, physical world understanding, and cinematic principle application.
- Score: 15.410503589735699
- License:
- 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.
Related papers
- TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive Interaction [25.851857218815415]
We introduce Theme-aware Video Dialogue Crafting (TVDC), a novel task aimed at generating new dialogues that align with video content and adhere to user-specified themes.
TV-Dialogue is a novel multi-modal agent framework that ensures both theme alignment and visual consistency.
Our findings underscore the potential of TV-Dialogue for various applications, such as video re-creation, film dubbing and its use in downstream multimodal tasks.
arXiv Detail & Related papers (2025-01-31T08:04:32Z) - I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in
Social Robots [0.040792653193642496]
This paper presents an initial implementation of a dialogue manager that enhances the traditional text-based prompts with real-time visual input.
The system's prompt engineering, incorporating dialogue with summarisation of the images, ensures a balance between context preservation and computational efficiency.
arXiv Detail & Related papers (2023-11-15T13:47:00Z) - Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs.
We employ domain-adaptive training strategies to help the model adapt to the dialogue domains.
Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
arXiv Detail & Related papers (2023-01-10T13:18:25Z) - A Benchmark for Understanding and Generating Dialogue between Characters
in Stories [75.29466820496913]
We present the first study to explore whether machines can understand and generate dialogue in stories.
We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition.
We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
arXiv Detail & Related papers (2022-09-18T10:19:04Z) - Back to the Future: Bidirectional Information Decoupling Network for
Multi-turn Dialogue Modeling [80.51094098799736]
We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder.
BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks.
Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
arXiv Detail & Related papers (2022-04-18T03:51:46Z) - Structural Modeling for Dialogue Disentanglement [43.352833140317486]
Multi-party dialogue context Tangled multi-party dialogue context leads to challenges for dialogue reading comprehension.
This work designs a novel model to disentangle multi-party history into threads, by taking dialogue structure features into account.
arXiv Detail & Related papers (2021-10-15T11:28:43Z) - Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence.
This paper reviews the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task.
In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios.
arXiv Detail & Related papers (2021-10-11T03:52:37Z) - DialogLM: Pre-trained Model for Long Dialogue Understanding and
Summarization [19.918194137007653]
We present a pre-training framework for long dialogue understanding and summarization.
Considering the nature of long conversations, we propose a window-based denoising approach for generative pre-training.
We conduct extensive experiments on five datasets of long dialogues, covering tasks of dialogue summarization, abstractive question answering and topic segmentation.
arXiv Detail & Related papers (2021-09-06T13:55:03Z) - Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
We review the previous methods from the perspective of dialogue modeling.
We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
arXiv Detail & Related papers (2021-03-04T15:50:17Z) - Video-Grounded Dialogues with Pretrained Generation Language Models [88.15419265622748]
We leverage the power of pre-trained language models for improving video-grounded dialogue.
We propose a framework by formulating sequence-to-grounded dialogue tasks as a sequence-to-grounded task.
Our framework allows fine-tuning language models to capture dependencies across multiple modalities.
arXiv Detail & Related papers (2020-06-27T08:24:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.