  Data-driven approaches for modeling human skeletal motion have found various
applications in interactive media and social robotics. Challenges remain in
these fields for generating high-fidelity samples and robustly reconstructing
motion from imperfect input data, due to e.g. missed marker detection. In this
paper, we propose a probabilistic generative model to synthesize and
reconstruct long horizon motion sequences conditioned on past information and
control signals, such as the path along which an individual is moving. Our
method adapts the existing work MoGlow by introducing a new graph-based model.
The model leverages the spatial-temporal graph convolutional network (ST-GCN)
to effectively capture the spatial structure and temporal correlation of
skeletal motion data at multiple scales. We evaluate the models on a mixture of
motion capture datasets of human locomotion with foot-step and bone-length
analysis. The results demonstrate the advantages of our model in reconstructing
missing markers and achieving comparable results on generating realistic future
poses. When the inputs are imperfect, our model shows improvements on
robustness of generation.
