title: Long-Video Audio Synthesis with Multi-Agent Collaboration
summary: Video-to-audio synthesis, which generates synchronized audio for visual
  content, critically enhances viewer immersion and narrative coherence in film
  and interactive media. However, video-to-audio dubbing for long-form content
  remains an unsolved challenge due to dynamic semantic shifts, temporal
  misalignment, and the absence of dedicated datasets. While existing methods
  excel in short videos, they falter in long scenarios (e.g., movies) due to
  fragmented synthesis and inadequate cross-scene consistency. We propose
  LVAS-Agent, a novel multi-agent framework that emulates professional dubbing
  workflows through collaborative role specialization. Our approach decomposes
  long-video synthesis into four steps including scene segmentation, script
  generation, sound design and audio synthesis. Central innovations include a
  discussion-correction mechanism for scene/script refinement and a
  generation-retrieval loop for temporal-semantic alignment. To enable systematic
  evaluation, we introduce LVAS-Bench, the first benchmark with 207
  professionally curated long videos spanning diverse scenarios. Experiments
  demonstrate superior audio-visual alignment over baseline methods. Project
  page: https://lvas-agent.github.io
paper_authors._id: 67d91dadb533888991ade4e1
paper_authors.name: Yehang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T08:07:19.937Z
paper_authors._id: 672c6f3d4c1e2de12c6f174e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/mv-8GX6OBBpJLzVvyPYbz.png
paper_authors.fullname: Yehang Zhang
paper_authors.type: user
paper_authors.user: Buzz-lightyear
paper_authors._id: 67d91dadb533888991ade4e2
paper_authors.name: Xinli Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:35:50.412Z
paper_authors._id: 64b4ab62eec33e27dcd733b5
paper_authors.avatarUrl: /avatars/0a9bf220c9a5efe7279f9b287b087d36.svg
paper_authors.fullname: Xinli XU
paper_authors.type: user
paper_authors.user: Xxlbigbrother
paper_authors._id: 67d91dadb533888991ade4e3
paper_authors.name: Xiaojie Xu
paper_authors._id: 67d91dadb533888991ade4e4
paper_authors.name: Li Liu
paper_authors._id: 67d91dadb533888991ade4e5
paper_authors.name: Yingcong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:36:19.820Z
paper_authors._id: 655cba1d87b67834000590e8
paper_authors.avatarUrl: /avatars/3bd43b7c9351f65b8f38f4c8237a0146.svg
paper_authors.fullname: Yingcong Chen
paper_authors.type: user
paper_authors.user: yingcongchen
paper_title: Long-Video Audio Synthesis with Multi-Agent Collaboration
paper_summary: Video-to-audio synthesis, which generates synchronized audio for visual
  content, critically enhances viewer immersion and narrative coherence in film
  and interactive media. However, video-to-audio dubbing for long-form content
  remains an unsolved challenge due to dynamic semantic shifts, temporal
  misalignment, and the absence of dedicated datasets. While existing methods
  excel in short videos, they falter in long scenarios (e.g., movies) due to
  fragmented synthesis and inadequate cross-scene consistency. We propose
  LVAS-Agent, a novel multi-agent framework that emulates professional dubbing
  workflows through collaborative role specialization. Our approach decomposes
  long-video synthesis into four steps including scene segmentation, script
  generation, sound design and audio synthesis. Central innovations include a
  discussion-correction mechanism for scene/script refinement and a
  generation-retrieval loop for temporal-semantic alignment. To enable systematic
  evaluation, we introduce LVAS-Bench, the first benchmark with 207
  professionally curated long videos spanning diverse scenarios. Experiments
  demonstrate superior audio-visual alignment over baseline methods. Project
  page: https://lvas-agent.github.io
paper_ai_summary: LVAS-Agent, a multi-agent framework, improves video-to-audio synthesis for long-form content through scene segmentation, script generation, sound design, and audio synthesis with a discussion-correction and generation-retrieval loop.
paper_ai_keywords: multi-agent framework scene segmentation script generation sound design audio synthesis discussion-correction mechanism generation-retrieval loop temporal-semantic alignment