MFOGCN: multi-feature-based orthogonal graph convolutional network for 3D human motion prediction-Reference-Cited by-同舟云学术

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MFOGCN: multi-feature-based orthogonal graph convolutional network for 3D human motion prediction

Published:2023-11-30 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Tu Jianfeng,Zang Tuo,Duan Mengran,Jiang Hanrui,Zhao Jiahui,Jiang Nan,Liu Lingfeng^ORCID

Funder

Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province

Natural Science Foundation of Jiangxi Province

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-023-03152-x.pdf

Reference45 articles.

1. Gui, L.Y., Zhang, K., Wang, Y.X., et al.: Teaching robots to predict human motion. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 562–567 (2018)

2. Singh, D., Srivastava, R.: Graph neural network with RNNS based trajectory prediction of dynamic agents for autonomous vehicle. Appl. Intell. 52(11), 12801–12816 (2022)

3. Ma, Q., Zou, Q., Huang, Y., et al.: Dynamic pedestrian trajectory forecasting with LSTM-based Delaunay triangulation. Appl. Intell. 52(3), 3018–3028 (2022)

4. Lehrmann, A.M., Gehler, P.V., Nowozin, S.: Efficient nonlinear markov models for human motion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1314–1321 (2014)

5. Wang, J.M., Fleet, D.J., Hertzmann, A.: Gaussian process dynamical models for human motion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 283–298 (2007)

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