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arXiv · 2002.05070

AlignNet: A Unifying Approach to Audio-Visual Alignment

Jianren Wang·Zhaoyuan Fang·Hang Zhao
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Abstract

We present AlignNet, a model that synchronizes videos with reference audios under non-uniform and irregular misalignments. AlignNet learns the end-to-end dense correspondence between each frame of a video and an audio. Our method is designed according to simple and well-established principles: attention, pyramidal processing, warping, and affinity function. Together with the model, we release a dancing dataset Dance50 for training and evaluation. Qualitative, quantitative and subjective evaluation results on dance-music alignment and speech-lip alignment demonstrate that our method far outperforms the state-of-the-art methods. Project video and code are available at https://jianrenw.github.io/AlignNet.

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BibTeXRIS

Jianren Wang, Zhaoyuan Fang, Hang Zhao. 2020-02-12. AlignNet: A Unifying Approach to Audio-Visual Alignment. https://arxiv.org/abs/2002.05070

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