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The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction.Various styles of graph convolutions have been proposed, with each one meticulously designed and incorporated into a carefully-crafted network architecture.
The graph neural network model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2008 · 2008
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Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Ionescu, C.; Papava, D.; Olaru, V.; and Sminchisescu, C. 2013 · 2013
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Recurrent network models for human dynamics
Fragkiadaki, K.; Levine, S.; Felsen, P.; and Malik, J. 2015 · 2015
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Categorical reparameterization with gumbel-softmax
Jang, E.; Gu, S.; and Poole, B. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
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Deep representation learning for human motion prediction and classification
Butepage, J.; Black, M. J.; Kragic, D.; and Kjellstrom, H. 2017 · 2017
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Learning human motion models for long-term predictions
Ghosh, P.; Song, J.; Aksan, E.; and Hilliges, O. 2017 · 2017
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Deep sets
Zaheer, M.; Kottur, S.; Ravanbakhsh, S.; Poczos, B.; Salakhutdinov, R. R.; and Smola, A. J. 2017 · 2017
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Convolutional sequence to sequence model for human dynamics
Li, C.; Zhang, Z.; Lee, W. S.; and Lee, G. H. 2018 · 2018
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Recovering accurate 3d human pose in the wild using imus and a moving camera
Von Marcard, T.; Henschel, R.; Black, M. J.; Rosenhahn, B.; and Pons-Moll, G. 2018 · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
Yan, S.; Xiong, Y.; and Lin, D. 2018 · 2018
Cited alongside, same era.
AMASS: Archive of motion capture as surface shapes
Mahmood, N.; Ghorbani, N.; Troje, N. F.; Pons-Moll, G.; and Black, M. J. 2019 · 2019
Cited alongside, same era.
Learning trajectory dependencies for human motion prediction
Mao, W.; Liu, M.; Salzmann, M.; and Li, H. 2019 · 2019
Cited alongside, same era.
Learning dynamic relationships for 3d human motion prediction
Cui, Q.; Sun, H.; and Yang, F. 2020 · 2020
Cited alongside, same era.
Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction
Li, M.; Chen, S.; Zhao, Y.; Zhang, Y.; Wang, Y.; and Tian, Q. 2020 · 2020
Cited alongside, same era.
Disentangling and unifying graph convolutions for skeleton-based action recognition
Multi-level motion attention for human motion prediction
Mao, W.; Liu, M.; Salzmann, M.; and Li, H. 2021 · 2021
Later among the works it cites.
Adasgn: Adapting joint number and model size for efficient skeleton-based action recognition
Shi, L.; Zhang, Y.; Cheng, J.; and Lu, H. 2021 · 2021
Later among the works it cites.
Space-time-separable graph convolutional network for pose forecasting
Sofianos, T.; Sampieri, A.; Franco, L.; and Galasso, F. 2021 · 2021
Later among the works it cites.
Motionmixer: mlp-based 3d human body pose forecasting
Bouazizi, A.; Holzbock, A.; Kressel, U.; Dietmayer, K.; and Belagiannis, V. 2022 · 2022
Later among the works it cites.
Generalized Pose Decoupled Network for Unsupervised 3d Skeleton Sequence-based Action Representation Learning
Liu, M.; Meng, F.; and Liang, Y. 2022 · 2022
Later among the works it cites.
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Liu, Z.; Zhang, H.; Chen, Z.; Wang, Z.; and Ouyang, W. 2020 · 2020
Cited alongside, same era.
History repeats itself: Human motion prediction via motion attention
Mao, W.; Liu, M.; and Salzmann, M. 2020 · 2020
Cited alongside, same era.
Channel-wise topology refinement graph convolution for skeleton-based action recognition
Chen, Y.; Zhang, Z.; Yuan, C.; Li, B.; Deng, Y.; and Hu, W. 2021 · 2021
Cited alongside, same era.
MSR-GCN: Multi-scale residual graph convolution networks for human motion prediction
Dang, L.; Nie, Y.; Long, C.; Zhang, Q.; and Li, G. 2021 · 2021
Cited alongside, same era.
Multiscale spatio-temporal graph neural networks for 3d skeleton-based motion prediction
Li, M.; Chen, S.; Zhao, Y.; Zhang, Y.; Wang, Y.; and Tian, Q. 2021 · 2021
Cited alongside, same era.
Progressively generating better initial guesses towards next stages for high-quality human motion prediction
Ma, T.; Nie, Y.; Long, C.; Zhang, Q.; and Li, G. 2022 · 2022
Later among the works it cites.
Spatio-temporal gating-adjacency GCN for human motion prediction
Zhong, C.; Hu, L.; Zhang, Z.; Ye, Y.; and Xia, S. 2022 · 2022
Later among the works it cites.
Back to mlp: A simple baseline for human motion prediction
Guo, W.; Du, Y.; Shen, X.; Lepetit, V.; Alameda-Pineda, X.; and Moreno-Noguer, F. 2023 · 2023
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Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
Liu, J.; Wang, X.; Wang, C.; Gao, Y.; and Liu, M. 2023 · 2023
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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
Wang, X.; Zhang, W.; Wang, C.; Gao, Y.; and Liu, M. 2024 · 2024
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