  Human skeleton point clouds are commonly used to automatically classify and
predict the behaviour of others. In this paper, we use a contrastive
self-supervised learning method, SimCLR, to learn representations that capture
the semantics of skeleton point clouds. This work focuses on systematically
evaluating the effects that different algorithmic decisions (including
augmentations, dataset partitioning and backbone architecture) have on the
learned skeleton representations. To pre-train the representations, we
normalise six existing datasets to obtain more than 40 million skeleton frames.
We evaluate the quality of the learned representations with three downstream
tasks: skeleton reconstruction, motion prediction, and activity classification.
Our results demonstrate the importance of 1) combining spatial and temporal
augmentations, 2) including additional datasets for encoder training, and 3)
and using a graph neural network as an encoder.
