  3D skeleton-based action recognition and motion prediction are two essential
problems of human activity understanding. In many previous works: 1) they
studied two tasks separately, neglecting internal correlations; 2) they did not
capture sufficient relations inside the body. To address these issues, we
propose a symbiotic model to handle two tasks jointly; and we propose two
scales of graphs to explicitly capture relations among body-joints and
body-parts. Together, we propose symbiotic graph neural networks, which contain
a backbone, an action-recognition head, and a motion-prediction head. Two heads
are trained jointly and enhance each other. For the backbone, we propose
multi-branch multi-scale graph convolution networks to extract spatial and
temporal features. The multi-scale graph convolution networks are based on
joint-scale and part-scale graphs. The joint-scale graphs contain actional
graphs, capturing action-based relations, and structural graphs, capturing
physical constraints. The part-scale graphs integrate body-joints to form
specific parts, representing high-level relations. Moreover, dual bone-based
graphs and networks are proposed to learn complementary features. We conduct
extensive experiments for skeleton-based action recognition and motion
prediction with four datasets, NTU-RGB+D, Kinetics, Human3.6M, and CMU Mocap.
Experiments show that our symbiotic graph neural networks achieve better
performances on both tasks compared to the state-of-the-art methods.
