Efficient convolutional hierarchical autoencoder for human motion prediction
Authors: Li, Y., Wang, Z., Yang, X., Wang, M., Poiana, S.I., Chaudhry, E. and Zhang, J.
Journal: Visual Computer
Volume: 35
Issue: 6-8
Pages: 1143-1156
ISSN: 0178-2789
DOI: 10.1007/s00371-019-01692-9
Abstract:Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.
https://eprints.bournemouth.ac.uk/32364/
Source: Scopus
Efficient convolutional hierarchical autoencoder for human motion prediction
Authors: Li, Y., Wang, Z., Yang, X., Wang, M., Poiana, S.I., Chaudhry, E. and Zhang, J.
Journal: VISUAL COMPUTER
Volume: 35
Issue: 6-8
Pages: 1143-1156
eISSN: 1432-2315
ISSN: 0178-2789
DOI: 10.1007/s00371-019-01692-9
https://eprints.bournemouth.ac.uk/32364/
Source: Web of Science (Lite)
Efficient convolutional hierarchical autoencoder for human motion prediction
Authors: Liu, Y., Wang, Z., Yang, X., Wang, M., Poiana, S.I., Chaudhry, E. and Zhang, J.J.
Journal: Visual Computer
Volume: 35
Issue: 6-8
Pages: 1143-1156
ISSN: 0178-2789
Abstract:© 2019, The Author(s). Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.
https://eprints.bournemouth.ac.uk/32364/
Source: BURO EPrints