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

• URL: http://arxiv.org/abs/2501.18940v1
• Date: Fri, 31 Jan 2025 08:04:32 GMT
• Title: TV-Dialogue: Crafting Theme-Aware Video Dialogues with Immersive Interaction
• Authors: Sai Wang, Fan Ma, Xinyi Li, Hehe Fan, Yu Wu,
• Abstract summary: 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.
• Score: 25.851857218815415
• License:
• 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.

Related papers

• Dialogue Director: Bridging the Gap in Dialogue Visualization for Multimodal Storytelling [15.410503589735699]
  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.
  arXiv  Detail & Related papers  (2024-12-30T05:54:23Z)
• DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling [73.08187964426823]
  Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction. This paper introduces a new research task--$textbfD$ialogue $textbfE$lement $textbfMO$deling. We propose a novel benchmark, $textbfDEMO$, designed for a comprehensive dialogue modeling and assessment.
  arXiv  Detail & Related papers  (2024-12-06T10:01:38Z)
• Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation [55.043492250775294]
  We introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response. We also introduce MultiDialog, the first large-scale multimodal spoken dialogue corpus.
  arXiv  Detail & Related papers  (2024-06-12T04:48:36Z)
• VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions [47.94531693056304]
  Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics. We present Video-grounded Scene&Topic AwaRe dialogue dataset, a large scale video-grounded dialogue understanding dataset based on 395 TV series.
  arXiv  Detail & Related papers  (2023-05-30T05:40:37Z)
• Multimodal Dialogue State Tracking [97.25466640240619]
  Video-Dialogue Transformer Network (VDTN) learns contextual dependencies between videos and dialogues to generate multimodal dialogue states. VDTN combines both object-level features and segment-level features and learns contextual dependencies between videos and dialogues to generate multimodal dialogue states.
  arXiv  Detail & Related papers  (2022-06-16T03:18:42Z)
• HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data [87.67278915655712]
  We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions.
  arXiv  Detail & Related papers  (2022-04-28T00:52:16Z)
• 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)
• 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)
• OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts [35.57757367869986]
  We release bf OpenViDial, a large-scale multi- module dialogue dataset. OpenViDial contains a total number of 1.1 million dialogue turns. We propose a family of encoder-decoder models leveraging both textual and visual contexts.
  arXiv  Detail & Related papers  (2020-12-30T03:02:50Z)

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.