Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

Understand

Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics.

• To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extractor.
• However, existing methods have limitations in achieving (1) unbiased long-range joint relationship modeling under multi-scale operators and (2) unobstructed cross-spacetime information flow for capturing complex spatial-temporal dependencies.
• In this work, we present (1) a simple method to disentangle multi-scale graph convolutions and (2) a unified spatial-temporal graph convolutional operator named G3D.