Papers › MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
Lingwei Dang, Yongwei Nie, Chengjiang Long, Qing Zhang, Guiqing Li
Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstraction level, the motion of the pose becomes more stable, which benefits pose prediction too. In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. The GCNs are used to extract features from fine to coarse scale and then from coarse to fine scale. The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses. Intermediate supervisions are imposed on all the predicted poses, which enforces the network to learn more representative features. Our proposed approach is evaluated on two standard benchmark datasets, i.e., the Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches. Code and pre-trained models are available at https://github.com/Droliven/MSRGCN.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human Pose Forecasting | Human3.6M | MSR-GCN | Average MPJPE (mm) @ 1000 ms | 114.2 | #13 of 33 | Archive leaderboard | report |
| Human Pose Forecasting | Human3.6M | MSR-GCN | Average MPJPE (mm) @ 400ms | 62.9 | #13 of 33 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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