  In this paper, we propose a novel graph convolutional network architecture,
Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The
proposed architecture consists of repeated encoder-decoder, in which
graph-structured features are processed across three different scales of human
skeletal representations. This multi-scale architecture enables the model to
learn both local and global feature representations, which are critical for 3D
human pose estimation. We also introduce a multi-level feature learning
approach using different-depth intermediate features and show the performance
improvements that result from exploiting multi-scale, multi-level feature
representations. Extensive experiments are conducted to validate our approach,
and the results show that our model outperforms the state-of-the-art.
