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This paper presents a 2D skeleton-based action segmentation method withapplications in fine-grained human activity recognition. In contrast withstate-of-the-art methods which directly take sequences of 3D skeletoncoordinates as inputs and apply Graph Convolutional Networks (GCNs) forspatiotemporal feature learning, our main idea is to use sequences of 2Dskeleton heatmaps as inputs and employ Temporal Convolutional Networks (TCNs)to extract spatiotemporal features. Despite lacking 3D information, ourapproach yields comparable/superior performances and better robustness againstmissing keypoints than previous methods on action segmentation datasets.Moreover, we improve the performances further by using both 2D skeletonheatmaps and RGB videos as inputs. To our best knowledge, this is the firstwork to utilize 2D skeleton heatmap inputs and the first work to explore 2Dskeleton+RGB fusion for action segmentation.

Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion