GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video

Bruce X.B. Yu, Zhi Zhang, Yongxu Liu, Sheng-hua Zhong, Yan Liu, Chang Wen Chen

Abstract:

3D human pose estimation has been researched for
decades with promising fruits. 3D human pose lifting is one
of the promising research directions toward the task where
both estimated pose and ground truth pose data are used
for training. Existing pose lifting works mainly focus on
improving the performance of estimated pose, but they usually
underperform when testing on the ground truth pose
data. We observe that the performance of the estimated
pose can be easily improved by preparing good quality 2D
pose, such as fine-tuning the 2D pose or using advanced
2D pose detectors. As such, we concentrate on improving
the 3D human pose lifting via ground truth data for the future
improvement of more quality estimated pose data. Towards
this goal, a simple yet effective model called Globallocal
Adaptive Graph Convolutional Network (GLA-GCN)
is proposed in this work. Our GLA-GCN globally models
the spatiotemporal structure via a graph representation
and backtraces local joint features for 3D human pose estimation
via individually connected layers. To validate our
model design, we conduct extensive experiments on three
benchmark datasets: Human3.6M, HumanEva-I, and MPIINF-
3DHP. Experimental results show that our GLA-GCN
implemented with ground truth 2D poses significantly outperforms
state-of-the-art methods (e.g., up to 3%, 17%, and
14% error reductions on Human3.6M, HumanEva-I, and
MPI-INF-3DHP, respectively).