Long-Video Audio Synthesis with Multi-Agent Collaboration
Authors: name: Yehang Zhang, name: Xinli Xu, name: Xiaojie Xu, name: Li Liu, name: Yingcong Chen

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