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The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13668))

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Abstract

Machine learning is transforming the video editing industry. Recent advances in computer vision have leveled-up video editing tasks such as intelligent reframing, rotoscoping, color grading, or applying digital makeups. However, most of the solutions have focused on video manipulation and VFX. This work introduces the Anatomy of Video Editing, a dataset, and benchmark, to foster research in AI-assisted video editing. Our benchmark suite focuses on video editing tasks, beyond visual effects, such as automatic footage organization and assisted video assembling. To enable research on these fronts, we annotate more than 1.5M tags, with relevant concepts to cinematography, from 196176 shots sampled from movie scenes. We establish competitive baseline methods and detailed analyses for each of the tasks. We hope our work sparks innovative research towards underexplored areas of AI-assisted video editing. Code is available at: https://github.com/dawitmureja/AVE.git.

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Notes

  1. 1.

    We crawled the movie scenes from the MovieClips YouTube Channel.

  2. 2.

    We consider 3 intensify patterns: extreme-wide - wide - medium, wide - medium - close-up, medium - close-up - extreme-close-up.

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Author information

Authors and Affiliations

  1. Adobe Research, San Jose, USA

    Dawit Mureja Argaw, Fabian Caba Heilbron, Joon-Young Lee & Markus Woodson

  2. KAIST, Daejeon, South Korea

    Dawit Mureja Argaw & In So Kweon

Authors
  1. Dawit Mureja Argaw
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  2. Fabian Caba Heilbron
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  3. Joon-Young Lee
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  4. Markus Woodson
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  5. In So Kweon
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Corresponding author

Correspondence to Dawit Mureja Argaw .

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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Argaw, D.M., Heilbron, F.C., Lee, JY., Woodson, M., Kweon, I.S. (2022). The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing. 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 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_12

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  • DOI: https://doi.org/10.1007/978-3-031-20074-8_12

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