TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive Interaction

2025-01-31

AI Summary

Existing video-based dialogue generation methods struggle to simultaneously ensure topic coherence and visual relevance. This paper introduces the novel task of “topic-aware video dialogue construction,” which aims to generate visually consistent and topic-focused dialogues by dynamically interpreting characters’ emotions and actions in videos, conditioned on user-specified topics. Methodologically, we propose the first dual-constraint framework integrating topic alignment and visual consistency; design a multimodal agent architecture supporting zero-shot adaptation to arbitrary video durations and open-ended topics; and establish a high-precision, interpretable, multi-granularity evaluation benchmark. Experiments demonstrate that our approach significantly outperforms state-of-the-art large language models on our curated dataset, enables end-to-end zero-shot video dialogue generation, and validates practical utility in real-world applications such as video re-editing and film dubbing.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

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
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Recent advancements in LLMs have accelerated the development of dialogue generation across text and images, yet video-based dialogue generation remains underexplored and presents unique challenges. In this paper, 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. We propose TV-Dialogue, a novel multi-modal agent framework that ensures both theme alignment (i.e., the dialogue revolves around the theme) and visual consistency (i.e., the dialogue matches the emotions and behaviors of characters in the video) by enabling real-time immersive interactions among video characters, thereby accurately understanding the video content and generating new dialogue that aligns with the given themes. To assess the generated dialogues, we present a multi-granularity evaluation benchmark with high accuracy, interpretability and reliability, demonstrating the effectiveness of TV-Dialogue on self-collected dataset over directly using existing LLMs. Extensive experiments reveal that TV-Dialogue can generate dialogues for videos of any length and any theme in a zero-shot manner without training. 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.

Problem

• Video Chat
• Content Consistency
• Relevance Preservation

Innovation

• Real-time Dialogue Generation
• Adaptive Video Content
• User-specified Themes