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Semantic visually-guided acoustic highlighting with large vision-language models

Junhua Huang, Chao Huang, Chenliang Xu

TL;DR

The paper investigates which visual semantics most effectively guide audio remixing in video by leveraging LVLM-derived textual cues. SemMix uses prompts to extract six visual aspects and conditions a lightweight gain-control pipeline, identifying camera focus, scene background, and tone as the strongest cues for perceptual mix quality. It demonstrates state-of-the-art improvements over audio-only and prior multimodal baselines with fewer parameters and shallower transformers, providing a practical framework for automating cinema-grade sound design. The findings offer a concrete path toward robust, text-guided audiovisual alignment using lightweight conditioning from LVLMs.

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.

Semantic visually-guided acoustic highlighting with large vision-language models

TL;DR

The paper investigates which visual semantics most effectively guide audio remixing in video by leveraging LVLM-derived textual cues. SemMix uses prompts to extract six visual aspects and conditions a lightweight gain-control pipeline, identifying camera focus, scene background, and tone as the strongest cues for perceptual mix quality. It demonstrates state-of-the-art improvements over audio-only and prior multimodal baselines with fewer parameters and shallower transformers, providing a practical framework for automating cinema-grade sound design. The findings offer a concrete path toward robust, text-guided audiovisual alignment using lightweight conditioning from LVLMs.

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.
Paper Structure (16 sections, 2 figures, 3 tables)

This paper contains 16 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Approach
  4. Task Overview
  5. Pipeline Overview
  6. SemMix: What Visual Semantics Matter?
  7. Prompt Versions.
  8. Experiment
  9. Experimental Setting
  10. Evaluation Metrics
  11. Baselines and Prior Work
  12. Analysis
  13. Prompt Comparison.
  14. Focused vs. Minimal Prompt Sets.
  15. Transformer Depth.
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Overview identical to VisAH huang2025visah except the text feature module (orange) feeding the context encoder.
  • Figure 2: Model performance using focused prompt Camera Focus from layer 0 to layer 6. shaded bands show cross-metric spread (min--max and IQR). The mean curve peaks at $L{=}3$; $L{=}5/6$ offer mild W-dis polish.