Papers › GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen, Mengyuan Liu
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. This paper breaks the limits of existing knowledge by proposing Universal Graph Convolution (UniGC), a novel graph convolution concept that re-conceptualizes different graph convolutions as its special cases. Leveraging UniGC on network-level, we propose GCNext, a novel GCN-building paradigm that dynamically determines the best-fitting graph convolutions both sample-wise and layer-wise. GCNext offers multiple use cases, including training a new GCN from scratch or refining a preexisting GCN. Experiments on Human3.6M, AMASS, and 3DPW datasets show that, by incorporating unique module-to-network designs, GCNext yields up to 9x lower computational cost than existing GCN methods, on top of achieving state-of-the-art performance.
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Code
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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 | 3DPW | GCNext | Average MPJPE (mm) 1000 msec | 72.0 | #3 of 7 | Archive leaderboard | report |
| Human Pose Forecasting | AMASS | GCNext | Average MPJPE (mm) 1000 msec | 65.3 | #2 of 11 | Archive leaderboard | report |
| Human Pose Forecasting | Human3.6M | GCNext | Average MPJPE (mm) @ 1000 ms | 64.7 | #1 of 33 | Archive leaderboard | report |
| Human Pose Forecasting | Human3.6M | GCNext | Average MPJPE (mm) @ 400ms | 30.5 | #1 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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