  3D hand pose estimation from RGB images suffers from the difficulty of
obtaining the depth information. Therefore, a great deal of attention has been
spent on estimating 3D hand pose from 2D hand joints. In this paper, we
leverage the advantage of spatial-temporal Graph Convolutional Neural Networks
and propose LG-Hand, a powerful method for 3D hand pose estimation. Our method
incorporates both spatial and temporal dependencies into a single process. We
argue that kinematic information plays an important role, contributing to the
performance of 3D hand pose estimation. We thereby introduce two new objective
functions, Angle and Direction loss, to take the hand structure into account.
While Angle loss covers locally kinematic information, Direction loss handles
globally kinematic one. Our LG-Hand achieves promising results on the
First-Person Hand Action Benchmark (FPHAB) dataset. We also perform an ablation
study to show the efficacy of the two proposed objective functions.
