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

• URL: http://arxiv.org/abs/2311.17408v1
• Date: Wed, 29 Nov 2023 07:25:49 GMT
• Title: Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
• Authors: Xinshun Wang, Wanying Zhang, Can Wang, Yuan Gao, Mengyuan Liu
• Abstract summary: This paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN) which constructs a dense graph and implements an integrated dynamic message passing. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages. Experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN.
• Score: 14.825185477750479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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