Multi-level Motion Attention for Human Motion Prediction
Multi-level Motion Attention for Human Motion Prediction
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New York: Springer US
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Language
English
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Publisher
New York: Springer US
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Contents
Human motion prediction aims to forecast future human poses given a historical motion. Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract
motion attention
to capture the similarity between the current motion context and the historical motion sub-sequences. In this context, we study the use of different types of attention, computed at joint, body part, and full pose levels. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW validate the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at
https://github.com/wei-mao-2019/HisRepItself
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Full title
Multi-level Motion Attention for Human Motion Prediction
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Record Identifier
TN_cdi_proquest_journals_2556151238
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2556151238
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ISSN
0920-5691
E-ISSN
1573-1405
DOI
10.1007/s11263-021-01483-7