  Graph Convolutional Networks (GCNs) have attracted increasing interests for
the task of skeleton-based action recognition. The key lies in the design of
the graph structure, which encodes skeleton topology information. In this
paper, we propose Dynamic GCN, in which a novel convolutional neural network
named Contextencoding Network (CeN) is introduced to learn skeleton topology
automatically. In particular, when learning the dependency between two joints,
contextual features from the rest joints are incorporated in a global manner.
CeN is extremely lightweight yet effective, and can be embedded into a graph
convolutional layer. By stacking multiple CeN-enabled graph convolutional
layers, we build Dynamic GCN. Notably, as a merit of CeN, dynamic graph
topologies are constructed for different input samples as well as graph
convolutional layers of various depths. Besides, three alternative context
modeling architectures are well explored, which may serve as a guideline for
future research on graph topology learning. CeN brings only ~7% extra FLOPs for
the baseline model, and Dynamic GCN achieves better performance with
$2\times$~$4\times$ fewer FLOPs than existing methods. By further combining
static physical body connections and motion modalities, we achieve
state-of-the-art performance on three large-scale benchmarks, namely NTU-RGB+D,
NTU-RGB+D 120 and Skeleton-Kinetics.
