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MVFI-Net: Motion-Aware Video Frame Interpolation Network

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13843))

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Abstract

Video frame interpolation (VFI) is to synthesize the intermediate frame given successive frames. Most existing learning-based VFI methods generate each target pixel by using the war** operation with either one predicted kernel or flow, or both. However, their performances are often degraded due to the issues on the limited direction and scope of the reference regions, especially encountering complex motions. In this paper, we propose a novel motion-aware VFI network (MVFI-Net) to address these issues. One of the key novelties of our method lies in the newly developed war** operation, i.e., motion-aware convolution (MAC). By predicting multiple extensible temporal motion vectors (MVs) and filter kernels for each target pixel, the direction and scope could be enlarged simultaneously. Besides, we first attempt to incorporate the pyramid structure into the kernel-based VFI, which can decompose large motions into smaller scales to improve the prediction efficiency. The quantitative and qualitative experimental results have demonstrated the proposed method delivers the state-of-the-art performance on the diverse benchmarks with various resolutions. Our codes are available at https://github.com/MediaLabVFI/MVFI-Net.

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Author information

Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xuhu Lin, Lili Zhao, ** Liu & Jianwen Chen

  2. China Mobile Research Institute, Bei**g, 100032, China

    Lili Zhao

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  1. Xuhu Lin
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  2. Lili Zhao
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  3. ** Liu
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  4. Jianwen Chen
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Corresponding author

Correspondence to Jianwen Chen .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Lin, X., Zhao, L., Liu, X., Chen, J. (2023). MVFI-Net: Motion-Aware Video Frame Interpolation Network. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13843. Springer, Cham. https://doi.org/10.1007/978-3-031-26313-2_21

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_21

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  • Online ISBN: 978-3-031-26313-2

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