  3D skeleton-based motion prediction and activity recognition are two
interwoven tasks in human behaviour analysis. In this work, we propose a motion
context modeling methodology that provides a new way to combine the advantages
of both graph convolutional neural networks and recurrent neural networks for
joint human motion prediction and activity recognition. Our approach is based
on using an LSTM encoder-decoder and a non-local feature extraction attention
mechanism to model the spatial correlation of human skeleton data and temporal
correlation among motion frames. The proposed network can easily include two
output branches, one for Activity Recognition and one for Future Motion
Prediction, which can be jointly trained for enhanced performance. Experimental
results on Human 3.6M, CMU Mocap and NTU RGB-D datasets show that our proposed
approach provides the best prediction capability among baseline LSTM-based
methods, while achieving comparable performance to other state-of-the-art
methods.
