  In skeleton-based action recognition, graph convolutional networks (GCNs),
which model human body skeletons using graphical components such as nodes and
connections, have achieved remarkable performance recently. However, current
state-of-the-art methods for skeleton-based action recognition usually work on
the assumption that the completely observed skeletons will be provided. This
may be problematic to apply this assumption in real scenarios since there is
always a possibility that captured skeletons are incomplete or noisy. In this
work, we propose a skeleton-based action recognition method which is robust to
noise information of given skeleton features. The key insight of our approach
is to train a model by maximizing the mutual information between normal and
noisy skeletons using a predictive coding manner. We have conducted
comprehensive experiments about skeleton-based action recognition with defected
skeletons using NTU-RGB+D and Kinetics-Skeleton datasets. The experimental
results demonstrate that our approach achieves outstanding performance when
skeleton samples are noised compared with existing state-of-the-art methods.
