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Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13679))

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Abstract

We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region. Therefore, the results tend to converge to the mean of the multi-modal possibilities. In this paper, we explicitly account for such motion ambiguity, allowing us to generate multiple plausible solutions all in sharp detail. The key idea is to introduce a motion guidance representation, which is a compact quantization of 2D optical flow with only four discrete motion directions. Conditioned on the motion guidance, the blur decomposition is led to a specific, unambiguous solution by using a novel two-stage decomposition network. We propose a unified framework for blur decomposition, which supports various interfaces for generating our motion guidance, including human input, motion information from adjacent video frames, and learning from a video dataset. Extensive experiments on synthesized datasets and real-world data show that the proposed framework is qualitatively and quantitatively superior to previous methods, and also offers the merit of producing physically plausible and diverse solutions. Code is available at https://github.com/zzh-tech/Animation-from-Blur.

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Acknowledgement

This work was supported by D-CORE Grant from Microsoft Research Asia, JSPS KAKENHI Grant Numbers 22H00529, and 20H05951, and JST, the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2108.

Author information

Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Zhihang Zhong, Yinqiang Zheng & Imari Sato

  2. Microsoft Research Asia, Beijing, China

    Xiao Sun, Zhirong Wu & Stephen Lin

  3. National Institute of Informatics, Tokyo, Japan

    Zhihang Zhong & Imari Sato

Authors
  1. Zhihang Zhong
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  2. Xiao Sun
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  3. Zhirong Wu
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  4. Yinqiang Zheng
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  5. Stephen Lin
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  6. Imari Sato
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Corresponding author

Correspondence to Yinqiang Zheng .

Editor information

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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Zhong, Z., Sun, X., Wu, Z., Zheng, Y., Lin, S., Sato, I. (2022). Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance. 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 13679. Springer, Cham. https://doi.org/10.1007/978-3-031-19800-7_35

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  • DOI: https://doi.org/10.1007/978-3-031-19800-7_35

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