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Scene-Adaptive Temporal Stabilisation for Video Colourisation Using Deep Video Priors

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13804))

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Abstract

Automatic image colourisation methods applied independently to each video frame usually lead to flickering artefacts or propagation of errors because of differences between neighbouring frames. While this can be partially solved using optical flow methods, complex scenarios such as the appearance of new objects in the scene limit the efficiency of such solutions. To address this issue, we propose application of blind temporal consistency, learned during the inference stage, to consistently adapt colourisation to the given frames. However, training at test time is extremely time-consuming and its performance is highly dependent on the content, motion, and length of the input video, requiring a large number of iterations to generalise to complex sequences with multiple shots and scene changes. This paper proposes a generalised framework for colourisation of complex videos with an optimised few-shot training strategy to learn scene-aware video priors. The proposed architecture is jointly trained to stabilise the input video and to cluster its frames with the aim of learning scene-specific modes. Experimental results show performance improvement in complex sequences while requiring less training data and significantly fewer iterations.

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Notes

  1. 1.

    https://www.videvo.net/.

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Author information

Authors and Affiliations

  1. BBC Research and Development, London, UK

    Marc Gorriz Blanch & Marta Mrak

  2. Dublin City University, Dublin, Ireland

    Noel O’Connor

Authors
  1. Marc Gorriz Blanch
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  2. Noel O’Connor
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  3. Marta Mrak
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Corresponding author

Correspondence to Marc Gorriz Blanch .

Editor information

Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Blanch, M.G., O’Connor, N., Mrak, M. (2023). Scene-Adaptive Temporal Stabilisation for Video Colourisation Using Deep Video Priors. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_41

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  • DOI: https://doi.org/10.1007/978-3-031-25069-9_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25068-2

  • Online ISBN: 978-3-031-25069-9

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