Dynamic Dense Graph Convolutional Network for Skeleton-based Human
  Motion Prediction
Xinshun Wang Wanying Zhang Can Wang Yuan Gao Mengyuan Liu
Graph Convolutional Networks (GCN) which typically follows a neural message
passing framework to model dependencies among skeletal joints has achieved high
success in skeleton-based human motion prediction task. Nevertheless, how to
construct a graph from a skeleton sequence and how to perform message passing
on the graph are still open problems, which severely affect the performance of
GCN. To solve both problems, this paper presents a Dynamic Dense Graph
Convolutional Network (DD-GCN), which constructs a dense graph and implements
an integrated dynamic message passing. More specifically, we construct a dense
graph with 4D adjacency modeling as a comprehensive representation of motion
sequence at different levels of abstraction. Based on the dense graph, we
propose a dynamic message passing framework that learns dynamically from data
to generate distinctive messages reflecting sample-specific relevance among
nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap
datasets verify the effectiveness of our DD-GCN which obviously outperforms
state-of-the-art GCN-based methods, especially when using long-term and our
proposed extremely long-term protocol.