Papers › Temporally Aligned Audio for Video with Autoregression
Temporally Aligned Audio for Video with Autoregression
Ilpo Viertola, Vladimir Iashin, Esa Rahtu
We introduce V-AURA, the first autoregressive model to achieve high temporal alignment and relevance in video-to-audio generation. V-AURA uses a high-framerate visual feature extractor and a cross-modal audio-visual feature fusion strategy to capture fine-grained visual motion events and ensure precise temporal alignment. Additionally, we propose VisualSound, a benchmark dataset with high audio-visual relevance. VisualSound is based on VGGSound, a video dataset consisting of in-the-wild samples extracted from YouTube. During the curation, we remove samples where auditory events are not aligned with the visual ones. V-AURA outperforms current state-of-the-art models in temporal alignment and semantic relevance while maintaining comparable audio quality. Code, samples, VisualSound and models are available at https://v-aura.notion.site
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Code
Syntology Ran 11 of 13 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 7 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video-to-Sound Generation | VGG-Sound | V-AURA | FAD | 1.92 | #5 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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