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Cross-Identity Motion Transfer for Arbitrary Objects Through Pose-Attentive Video Reassembling

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

We propose an attention-based networks for transferring motions between arbitrary objects. Given a source image(s) and a driving video, our networks animate the subject in the source images according to the motion in the driving video. In our attention mechanism, dense similarities between the learned keypoints in the source and the driving images are computed in order to retrieve the appearance information from the source images. Taking a different approach from the well-studied warping based models, our attention-based model has several advantages. By reassembling non-locally searched pieces from the source contents, our approach can produce more realistic outputs. Furthermore, our system can make use of multiple observations of the source appearance (e.g. front and sides of faces) to make the results more accurate. To reduce the training-testing discrepancy of the self-supervised learning, a novel cross-identity training scheme is additionally introduced. With the training scheme, our networks is trained to transfer motions between different subjects, as in the real testing scenario. Experimental results validate that our method produces visually pleasing results in various object domains, showing better performances compared to previous works.

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Acknowledgement

This work was conducted by Center for Applied Research in Artificial Intelligence (CARAI) grant funded by DAPA and ADD (UD190031RD).

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Authors and Affiliations

  1. Yonsei University, Sinchon-dong, South Korea

    Subin Jeon, Seonghyeon Nam, Seoung Wug Oh & Seon Joo Kim

  2. Facebook, Menlo Park, USA

    Seon Joo Kim

Authors
  1. Subin Jeon
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  2. Seonghyeon Nam
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  3. Seoung Wug Oh
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  4. Seon Joo Kim
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Corresponding author

Correspondence to Subin Jeon .

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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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Jeon, S., Nam, S., Oh, S.W., Kim, S.J. (2020). Cross-Identity Motion Transfer for Arbitrary Objects Through Pose-Attentive Video Reassembling. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_18

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  • DOI: https://doi.org/10.1007/978-3-030-58586-0_18

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