Efficient convolutional hierarchical autoencoder for human motion prediction

Yanran Li, Zhao Wang, Xiaosong Yang, Meili Wang, Sebastian Iulian Poiana, Ehtzaz Chaudhry, Jianjun Zhang

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1143-1156
Number of pages14
JournalThe Visual Computer
Issue number6
Early online date11 May 2019
Publication statusPublished - 1 Jun 2019


  • Motion prediction
  • Deep learning
  • Autoencoder
  • Hierarchical networks


Dive into the research topics of 'Efficient convolutional hierarchical autoencoder for human motion prediction'. Together they form a unique fingerprint.

Cite this