Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Representations can be broadly categorized into two groups, depending on whether they use RGB-D information or 3D skeleton data. Recently, skeletonbased human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of benchmark datasets with skeleton data, and discuss potential future research directions.

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