In this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To skeleton-action recognition in wild, introduce Skeletics-152, a curated 3-D pose-annotated subset RGB videos sourced from Kinetics-700, large-scale dataset. We extend our to include out-of-context actions by introducing Skeleton-Mimetics, dataset derived recently introduced...

Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based recognition, skeleton-based is a safer way protect privacy subjects while having competitive performance. However, improvements in skeleton algorithms as well motion depth sensors, more details characteristics can be preserved dataset, leadin...

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