  Human motion prediction is still an open problem extremely important for
autonomous driving and safety applications. Due to the complex spatiotemporal
relation of motion sequences, this remains a challenging problem not only for
movement prediction but also to perform a preliminary interpretation of the
joint connections. In this work, we present a Context-based Interpretable
Spatio-Temporal Graph Convolutional Network (CIST-GCN), as an efficient 3D
human pose forecasting model based on GCNs that encompasses specific layers,
aiding model interpretability and providing information that might be useful
when analyzing motion distribution and body behavior. Our architecture extracts
meaningful information from pose sequences, aggregates displacements and
accelerations into the input model, and finally predicts the output
displacements. Extensive experiments on Human 3.6M, AMASS, 3DPW, and ExPI
datasets demonstrate that CIST-GCN outperforms previous methods in human motion
prediction and robustness. Since the idea of enhancing interpretability for
motion prediction has its merits, we showcase experiments towards it and
provide preliminary evaluations of such insights here. available code:
https://github.com/QualityMinds/cistgcn
