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Learning-based Axial Video Motion Magnification

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15112))

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Abstract

Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still retain complex characteristics even after being magnified, which likely distracts us from analyzing them. In this work, we focus on improving legibility by proposing a new concept, axial video motion magnification, which magnifies decomposed motions along the user-specified direction. Axial video motion magnification can be applied to various applications where motions of specific axes are critical, by providing simplified and easily readable motion information. To achieve this, we propose a novel Motion Separation Module that enables the disentangling and magnifying of motion representation along axes of interest. Furthermore, we build a new synthetic training dataset for our task that is generalized to real data. Our proposed method improves the legibility of resulting motions along certain axes by adding a new feature: user controllability. In addition, axial video motion magnification is a more generalized concept; thus, our method can be directly adapted to the generic motion magnification and achieves favorable performance against competing methods. The code and dataset are available on our project page: https://axial-momag.github.io/axial-momag/.

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Notes

  1. 1.

    We reproduced all the results using the codes publicly accessible.

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Acknowledgment

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant (No. RS-2022-II220290 (2022-0-00290), Visual Intelligence for Space-Time Understanding and Generation based on Multi-layered Visual Common Sense); No. RS-2019-II191906, Artificial Intelligence Graduate School Program (POSTECH), and National Research Foundation of Korea (NRF) grant (No. RS-2024-00358135, Corner Vision: Learning to Look Around the Corner through Multi-modal Signals) funded by the Korea government (MSIT)

Author information

Authors and Affiliations

  1. Graduate School of AI, POSTECH, Pohang-si, South Korea

    Kwon Byung-Ki & Tae-Hyun Oh

  2. Department of Electrical Engineering, POSTECH, Pohang-si, South Korea

    Oh Hyun-Bin, Kim Jun-Seong, Hyunwoo Ha & Tae-Hyun Oh

  3. Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, South Korea

    Tae-Hyun Oh

Authors
  1. Kwon Byung-Ki
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  2. Oh Hyun-Bin
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  3. Kim Jun-Seong
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  4. Hyunwoo Ha
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  5. Tae-Hyun Oh
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Corresponding author

Correspondence to Kwon Byung-Ki .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Byung-Ki, K., Hyun-Bin, O., Jun-Seong, K., Ha, H., Oh, TH. (2025). Learning-based Axial Video Motion Magnification. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15112. Springer, Cham. https://doi.org/10.1007/978-3-031-72949-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-72949-2_11

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