mlsanityData to 14 Oct 2026

AlignNet: A Unifying Approach to Audio-Visual Alignment

arXiv 2002.05070 ↗First posted 12 Feb 2020cs.CV cs.LG cs.MMcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Feb 2020.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 1; 2025: 3; 2026: 1.

13 papers.

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