  Spatio-temporal information is key to resolve occlusion and depth ambiguity
in 3D pose estimation. Previous methods have focused on either temporal
contexts or local-to-global architectures that embed fixed-length
spatio-temporal information. To date, there have not been effective proposals
to simultaneously and flexibly capture varying spatio-temporal sequences and
effectively achieves real-time 3D pose estimation. In this work, we improve the
learning of kinematic constraints in the human skeleton: posture, local
kinematic connections, and symmetry by modeling local and global spatial
information via attention mechanisms. To adapt to single- and multi-frame
estimation, the dilated temporal model is employed to process varying skeleton
sequences. Also, importantly, we carefully design the interleaving of spatial
semantics with temporal dependencies to achieve a synergistic effect. To this
end, we propose a simple yet effective graph attention spatio-temporal
convolutional network (GAST-Net) that comprises of interleaved temporal
convolutional and graph attention blocks. Experiments on two challenging
benchmark datasets (Human3.6M and HumanEva-I) and YouTube videos demonstrate
that our approach effectively mitigates depth ambiguity and self-occlusion,
generalizes to half upper body estimation, and achieves competitive performance
on 2D-to-3D video pose estimation. Code, video, and supplementary information
is available at:
\href{http://www.juanrojas.net/gast/}{http://www.juanrojas.net/gast/}
