Temporally Aligned Audio for Video with Autoregression
<|reference_start|>Temporally Aligned Audio for Video with Autoregression: 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<|reference_end|>
@article{viertola2024temporally,
    title={Temporally Aligned Audio for Video with Autoregression},
    author={Ilpo Viertola, Vladimir Iashin, Esa Rahtu},
    journal={arXiv preprint arXiv:2409.13689},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13689},
    primaryClass={cs.CV cs.MM cs.SD eess.AS}
}