  The ability to identify and temporally segment fine-grained actions in motion
capture sequences is crucial for applications in human movement analysis.
Motion capture is typically performed with optical or inertial measurement
systems, which encode human movement as a time series of human joint locations
and orientations or their higher-order representations. State-of-the-art action
segmentation approaches use multiple stages of temporal convolutions. The main
idea is to generate an initial prediction with several layers of temporal
convolutions and refine these predictions over multiple stages, also with
temporal convolutions. Although these approaches capture long-term temporal
patterns, the initial predictions do not adequately consider the spatial
hierarchy among the human joints. To address this limitation, we recently
introduced multi-stage spatial-temporal graph convolutional neural networks
(MS-GCN). Our framework replaces the initial stage of temporal convolutions
with spatial graph convolutions and dilated temporal convolutions, which better
exploit the spatial configuration of the joints and their long-term temporal
dynamics. Our framework was compared to four strong baselines on five tasks.
Experimental results demonstrate that our framework is a strong baseline for
skeleton-based action segmentation.
