Revisiting Skeleton-based Action Recognition

Human skeleton, as a compact representation of human action, has received increasing attention in recent years. Once fused with other modalities, it achieves the advanced on all eight multi-modality action recognition benchmarks.

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• 1 Many skeleton-based action recognition methods adopt GCNs to extract features on top of human skeletons.
• 2 Despite the positive results shown in these attempts, GCN-based methods are subject to limitations in robustness, interoperability, and scalability.
• 3 In this work, we propose PoseConv3D, a new approach to skeleton-based action recognition.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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