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Several applications such as autonomous driving, augmented reality andvirtual reality require a precise prediction of the 3D human pose. Recently, anew problem was introduced in the field to predict the 3D human poses fromobserved 2D poses. We propose Skeleton-Graph, a deep spatio-temporal graph CNNmodel that predicts the future 3D skeleton poses in a single pass from the 2Dones. Unlike prior works, Skeleton-Graph focuses on modeling the interactionbetween the skeleton joints by exploiting their spatial configuration. This isbeing achieved by formulating the problem as a graph structure while learning asuitable graph adjacency kernel. By the design, Skeleton-Graph predicts thefuture 3D poses without divergence in the long-term, unlike prior works. Wealso introduce a new metric that measures the divergence of predictions in thelong term. Our results show an FDE improvement of at least 27% and an ADE of 4%on both the GTA-IM and PROX datasets respectively in comparison with priorworks. Also, we are 88% and 93% less divergence on the long-term motionprediction in comparison with prior works on both GTA-IM and PROX datasets.Code is available at https://github.com/abduallahmohamed/Skeleton-Graph.git

Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs