  Human pose estimation is a fundamental yet challenging task in computer
vision. Although deep learning techniques have made great progress in this
area, difficult scenarios (e.g., invisible keypoints, occlusions, complex
multi-person scenarios, and abnormal poses) are still not well-handled. To
alleviate these issues, we propose a novel Spatial Preserve and Content-aware
Network(SPCNet), which includes two effective modules: Dilated Hourglass
Module(DHM) and Selective Information Module(SIM). By using the Dilated
Hourglass Module, we can preserve the spatial resolution along with large
receptive field. Similar to Hourglass Network, we stack the DHMs to get the
multi-stage and multi-scale information. Then, a Selective Information Module
is designed to select relatively important features from different levels under
a sufficient consideration of spatial content-aware mechanism and thus
considerably improves the performance. Extensive experiments on MPII, LSP and
FLIC human pose estimation benchmarks demonstrate the effectiveness of our
network. In particular, we exceed previous methods and achieve the
state-of-the-art performance on three aforementioned benchmark datasets.
