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Real-Time Intermediate Flow Estimation for Video Frame Interpolation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13674))

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Abstract

Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4–27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. https://github.com/megvii-research/ECCV2022-RIFE

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Acknowledgement

This work is supported by National Key R &D Program of China (2021ZD0109803) and National Natural Science Foundation of China under Grant No. 62136001, 62088102.

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Authors and Affiliations

  1. Megvii Research, Bei**g, China

    Zhewei Huang, Tianyuan Zhang, Wen Heng & Shuchang Zhou

  2. NERCVT, School of Computer Science, Peking University, Bei**g, China

    Boxin Shi

  3. Institute for Artificial Intelligence, Peking University, Bei**g, China

    Boxin Shi

  4. Bei**g Academy of Artificial Intelligence, Bei**g, China

    Boxin Shi

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  1. Zhewei Huang
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  2. Tianyuan Zhang
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  3. Wen Heng
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  4. Boxin Shi
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  5. Shuchang Zhou
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Correspondence to Boxin Shi or Shuchang Zhou .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Huang, Z., Zhang, T., Heng, W., Shi, B., Zhou, S. (2022). Real-Time Intermediate Flow Estimation for Video Frame Interpolation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_36

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