Journal
VISUAL COMPUTER
Volume 35, Issue 6-8, Pages 1143-1156Publisher
SPRINGER
DOI: 10.1007/s00371-019-01692-9
Keywords
Motion prediction; Deep learning; Autoencoder; Hierarchical networks
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Funding
- EU H2020 under the REA grant agreement [691215]
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China [2018AIOT-09]
- South West Creative Technology Network
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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.
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