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Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

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Abstract

Video style transfer techniques inspire many exciting applications on mobile devices. However, their efficiency and stability are still far from satisfactory. To boost the transfer stability across frames, optical flow is widely adopted, despite its high computational complexity, e.g. occupying over 97% inference time. This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm. We adopt two teacher networks, one of which takes optical flow during inference while the other does not. The output difference between these two teacher networks highlights the improvements made by optical flow, which is then adopted to distill the target student network. Furthermore, a low-rank distillation loss is employed to stabilize the output of student network by mimicking the rank of input videos. Extensive experiments demonstrate that our student network without an optical flow module is still able to generate stable video and runs much faster than the teacher network.

X. Chen and Y. Zhang—Equal Contribution.

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Acknowledgments

We thank anonymous reviewers for their helpful comments. Chang Xu was supported by the Australian Research Council under Project DE180101438.

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Authors and Affiliations

  1. Noah’s Ark Lab, Huawei Technologies, Shenzhen, China

    Xinghao Chen, Yiman Zhang, Yunhe Wang, Han Shu & Chunjing Xu

  2. School of Computer Science, Faculty of Engineering, University of Sydney, Sydney, Australia

    Chang Xu

Authors
  1. Xinghao Chen
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  2. Yiman Zhang
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  3. Yunhe Wang
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  4. Han Shu
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  5. Chunjing Xu
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  6. Chang Xu
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Corresponding author

Correspondence to Xinghao Chen .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Chen, X., Zhang, Y., Wang, Y., Shu, H., Xu, C., Xu, C. (2020). Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_37

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_37

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  • Online ISBN: 978-3-030-58539-6

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