  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.
