  Human pose estimation plays an important role in many computer vision tasks
and has been studied for many decades. However, due to complex appearance
variations from poses, illuminations, occlusions and low resolutions, it still
remains a challenging problem. Taking the advantage of high-level semantic
information from deep convolutional neural networks is an effective way to
improve the accuracy of human pose estimation. In this paper, we propose a
novel Cascade Feature Aggregation (CFA) method, which cascades several
hourglass networks for robust human pose estimation. Features from different
stages are aggregated to obtain abundant contextual information, leading to
robustness to poses, partial occlusions and low resolution. Moreover, results
from different stages are fused to further improve the localization accuracy.
The extensive experiments on MPII datasets and LIP datasets demonstrate that
our proposed CFA outperforms the state-of-the-art and achieves the best
performance on the state-of-the-art benchmark MPII.
