dorsal/arxiv
View SchemaSemantic visually-guided acoustic highlighting with large vision-language models
| Authors | Junhua Huang, Chao Huang, Chenliang Xu |
|---|---|
| Categories | |
| ArXiv ID | 2601.08871vv1 |
| URL | https://arxiv.org/abs/2601.08871 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Balancing dialogue, music, and sound effects with accompanying video is crucial for immersive storytelling, yet current audio mixing workflows remain largely manual and labor-intensive. While recent advancements have introduced the visually guided acoustic highlighting task, which implicitly rebalances audio sources using multimodal guidance, it remains unclear which visual aspects are most effective as conditioning signals.We address this gap through a systematic study of whether deep video understanding improves audio remixing. Using textual descriptions as a proxy for visual analysis, we prompt large vision-language models to extract six types of visual-semantic aspects, including object and character appearance, emotion, camera focus, tone, scene background, and inferred sound-related cues. Through extensive experiments, camera focus, tone, and scene background consistently yield the largest improvements in perceptual mix quality over state-of-the-art baselines. Our findings (i) identify which visual-semantic cues most strongly support coherent and visually aligned audio remixing, and (ii) outline a practical path toward automating cinema-grade sound design using lightweight guidance derived from large vision-language models.
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"abstract": "Balancing dialogue, music, and sound effects with accompanying video is crucial for immersive storytelling, yet current audio mixing workflows remain largely manual and labor-intensive. While recent advancements have introduced the visually guided acoustic highlighting task, which implicitly rebalances audio sources using multimodal guidance, it remains unclear which visual aspects are most effective as conditioning signals.We address this gap through a systematic study of whether deep video understanding improves audio remixing. Using textual descriptions as a proxy for visual analysis, we prompt large vision-language models to extract six types of visual-semantic aspects, including object and character appearance, emotion, camera focus, tone, scene background, and inferred sound-related cues. Through extensive experiments, camera focus, tone, and scene background consistently yield the largest improvements in perceptual mix quality over state-of-the-art baselines. Our findings (i) identify which visual-semantic cues most strongly support coherent and visually aligned audio remixing, and (ii) outline a practical path toward automating cinema-grade sound design using lightweight guidance derived from large vision-language models.",
"arxiv_id": "2601.08871",
"authors": [
"Junhua Huang",
"Chao Huang",
"Chenliang Xu"
],
"categories": [
"cs.SD",
"cs.AI",
"eess.AS"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Semantic visually-guided acoustic highlighting with large vision-language models",
"url": "https://arxiv.org/abs/2601.08871",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
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