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A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction

A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction

Abstract

Recurrent neural network (RNN) has become popular for human motion prediction thanks to its ability to capture temporal dependencies. However, it has limited capacity in modeling the complex spatial relationship in the human skeletal structure. In this work, we present a novel diffusion convolutional recurrent predictor for spatial and temporal movement forecasting, with multi-step random walks traversing bidirectionally along an adaptive graph to model interdependency among body joints. In the temporal domain, existing methods rely on a single forward predictor with the produced motion deflecting to the drift route, which leads to error accumulations over time. We propose to supplement the forward predictor with a forward discriminator to alleviate such motion drift in the long term under adversarial training. The solution is further enhanced by a backward predictor and a backward discriminator to effectively reduce the error, such that the system can also look into the past to improve the prediction at early frames. The two-way spatial diffusion convolutions and two-way temporal predictors together form a quadruple network. Furthermore, we train our framework by modeling the velocity from observed motion dynamics instead of static poses to predict future movements that effectively reduces the discontinuity problem at early prediction. Our method outperforms the state of the arts on both 3D and 2D datasets, including the Human3.6M, CMU Motion Capture and Penn Action datasets. The results also show that our method correctly predicts both high-dynamic and low-dynamic moving trends with less motion drift.

Publication

Qianhui Men, Edmond S. L. Ho, Hubert P. H. Shum and Howard Leung,
"A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction",
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
, 2021
Impact Factor: 4.685# Citation: 5##

# Impact factors from the Journal Citation Reports 2020
## Citation counts from Google Scholar as of 2021

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References

BibTeX

@article{men21quadruple,
 author={Men, Qianhui and Ho, Edmond S. L. and Shum, Hubert P. H. and Leung, Howard},
 journal={IEEE Transactions on Circuits and Systems for Video Technology},
 title={A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction},
 year={2021},
 volume={31},
 number={9},
 pages={3417--3432},
 numpages={16},
 doi={10.1109/TCSVT.2020.3038145},
 issn={1051-8215},
 publisher={IEEE},
}

RIS

TY  - JOUR
AU  - Men, Qianhui
AU  - Ho, Edmond S. L.
AU  - Shum, Hubert P. H.
AU  - Leung, Howard
T2  - IEEE Transactions on Circuits and Systems for Video Technology
TI  - A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction
PY  - 2021
VL  - 31
IS  - 9
SP  - 3417
EP  - 3432
DO  - 10.1109/TCSVT.2020.3038145
SN  - 1051-8215
PB  - IEEE
ER  - 

Plain Text

Qianhui Men, Edmond S. L. Ho, Hubert P. H. Shum and Howard Leung, "A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction," IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 9, pp. 3417-3432, IEEE, 2021.

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