Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
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With potential applications in fields including intelligent surveillance and human-robot interaction, the human motion prediction task has become a hot research topic and also has achieved high success, especially using the recent Graph Convolutional Network (GCN). Current human motion prediction task usually focuses on predicting human motions for atomic actions. Observing that atomic actions can happen at the same time and thus formulating the composite actions, we propose the composite human motion prediction task. To handle this task, we first present a Composite Action Generation (CAG) module to generate synthetic composite actions for training, thus avoiding the laborious work of collecting composite action samples. Moreover, we alleviate the effect of composite actions on demand for a more complicated model by presenting a Dynamic Compositional Graph Convolutional Network (DC-GCN). Extensive experiments on the Human3.6M dataset and our newly collected CHAMP dataset consistently verify the efficiency of our DC-GCN method, which achieves state-of-the-art motion prediction accuracies and meanwhile needs few extra computational costs than traditional GCN-based human motion methods.
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Deep learning technologies including CNN, RNN, and LSTM have been widely applied to motion intent prediction.
“Deep learning, technologies including Convolutional Neural Network (CNN) 15,16 , Recurrent Neural Network (RNN) [17][18][19] , and Long Short-Term Memory network (LSTM) [20][21][22] have been widely applied to motion intent prediction.”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [15]
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Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
- Authors
- Zhang, W., Zhao, S., Meng, F., Wu, S., & Liu, M.
- Affiliations
- Sun Yat-sen University (CN)
- Peng Cheng Laboratory (CN)
- Sony (China) (CN)
- Peking University (CN)
- Venue
- Proceedings of the 31st ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2023-10-26
- Pages
- 2856-2864
- OpenAlex
- W4388186997
- Cited by
- 10
- References
- 39
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 5
- FWCI
- 4.27
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62203476)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Keywords
- human motion prediction, composite action, graph convolutional networks, dynamic graph convolution, atomic actions, Human3.6M, intelligent surveillance, human-robot interaction
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