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Deep representation learning for human motion prediction and classification
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
2017 (English)In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), IEEE, 2017, p. 1591-1599Conference paper (Refereed)
Abstract [en]

Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.

Place, publisher, year, edition, pages
IEEE, 2017. p. 1591-1599
Series
IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-221047DOI: 10.1109/CVPR.2017.173ISI: 000418371401068ISBN: 978-1-5386-0457-1 OAI: oai:DiVA.org:kth-221047DiVA, id: diva2:1172970
Conference
30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), JUL 21-26, 2016, Honolulu, HI
Funder
Swedish Foundation for Strategic Research
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-01-11Bibliographically approved

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Butepage, Judith

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  • apa
  • harvard1
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Output format
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