Attention-Based Recurrent Autoencoder for Motion Capture Denoising
Authors: Zhu, Y., Zhang, F. and Xiao, Z.
Journal: Journal of Internet Technology
Volume: 23
Issue: 6
Pages: 1325-1333
eISSN: 2079-4029
ISSN: 1607-9264
DOI: 10.53106/160792642022112306015
Abstract:To resolve the problem of massive loss of MoCap data from optical motion capture, we propose a novel network architecture based on attention mechanism and recurrent network. Its advantage is that the use of encoder-decoder enables automatic human motion manifold learning, capturing the hidden spatial-temporal relationships in motion sequences. In addition, by using the multi-head attention mechanism, it is possible to identify the most relevant corrupted frames with specific position information to recovery the missing markers, which can lead to more accurate motion reconstruction. Simulation experiments demonstrate that the network model we proposed can effectively handle the large-scale missing markers problem with better robustness, smaller errors and more natural recovered motion sequence compared to the reference method.
https://eprints.bournemouth.ac.uk/37857/
Source: Scopus
Attention-Based Recurrent Autoencoder for Motion Capture Denoising
Authors: Zhu, Y., Zhang, F. and Xiao, Z.
Journal: JOURNAL OF INTERNET TECHNOLOGY
Volume: 23
Issue: 6
Pages: 1325-1333
eISSN: 2079-4029
ISSN: 1607-9264
DOI: 10.53106/160792642022112306015
https://eprints.bournemouth.ac.uk/37857/
Source: Web of Science (Lite)
Attention-Based Recurrent Autoencoder for Motion Capture Denoising
Authors: Zhu, Y., Zhang, F. and Xiao, Z.
Journal: Journal of Internet Technology
Volume: 23
Issue: 6
Pages: 1325-1333
Publisher: Taiwan Academic Network Management Committee
ISSN: 1607-9264
https://eprints.bournemouth.ac.uk/37857/
Source: Manual
Attention-Based Recurrent Autoencoder for Motion Capture Denoising
Authors: Zhu, Y., Zhang, F. and Xiao, Z.
Journal: Journal of Internet Technology
Volume: 23
Issue: 6
Pages: 1325-1333
Publisher: Taiwan Academic Network Management Committee
ISSN: 1607-9264
Abstract:To resolve the problem of massive loss of MoCap data from optical motion capture, we propose a novel network architecture based on attention mechanism and recurrent network. Its advantage is that the use of encoder-decoder enables automatic human motion manifold learning, capturing the hidden spatial-temporal relationships in motion sequences. In addition, by using the multi-head attention mechanism, it is possible to identify the most relevant corrupted frames with specific position information to recovery the missing markers, which can lead to more accurate motion reconstruction. Simulation experiments demonstrate that the network model we proposed can effectively handle the large-scale missing markers problem with better robustness, smaller errors and more natural recovered motion sequence compared to the reference method.
https://eprints.bournemouth.ac.uk/37857/
Source: BURO EPrints