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The past few years has witnessed the dominance of Graph ConvolutionalNetworks (GCNs) over human motion prediction.Various styles of graphconvolutions have been proposed, with each one meticulously designed andincorporated into a carefully-crafted network architecture. This paper breaksthe limits of existing knowledge by proposing Universal Graph Convolution(UniGC), a novel graph convolution concept that re-conceptualizes differentgraph convolutions as its special cases. Leveraging UniGC on network-level, wepropose GCNext, a novel GCN-building paradigm that dynamically determines thebest-fitting graph convolutions both sample-wise and layer-wise. GCNext offersmultiple use cases, including training a new GCN from scratch or refining apreexisting GCN. Experiments on Human3.6M, AMASS, and 3DPW datasets show that,by incorporating unique module-to-network designs, GCNext yields up to 9x lowercomputational cost than existing GCN methods, on top of achievingstate-of-the-art performance.

GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction