This repository implements MS-G3D, a CVPR 2020 Oral paper on skeleton-based action recognition using graph convolutional networks. The model disentangles and unifies graph convolutions to better capture spatial-temporal patterns in human skeleton data. It supports training on datasets like NTU RGB+D 60/120 and Kinetics 400, and includes pretrained model weights for evaluation.