Dynamic Dense Graph Convolutional Network for Skeleton-Based Human Motion Prediction

Xinshun Wang

doi:10.1109/TIP.2023.3334954

Dynamic Dense Graph Convolutional Network for Skeleton-Based Human Motion Prediction

2024-01-01

22

OA

AI

DOI：10.1109/TIP.2023.3334954

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Human motion prediction

skeleton sequence

graph convolutional network

Dynamic Dense Graph Convolutional Network for Skeleton-Based Human Motion Prediction

Abstract

Journal

Organization