  Although occlusion widely exists in nature and remains a fundamental
challenge for pose estimation, existing heatmap-based approaches suffer serious
degradation on occlusions. Their intrinsic problem is that they directly
localize the joints based on visual information; however, the invisible joints
are lack of that. In contrast to localization, our framework estimates the
invisible joints from an inference perspective by proposing an Image-Guided
Progressive GCN module which provides a comprehensive understanding of both
image context and pose structure. Moreover, existing benchmarks contain limited
occlusions for evaluation. Therefore, we thoroughly pursue this problem and
propose a novel OPEC-Net framework together with a new Occluded Pose (OCPose)
dataset with 9k annotated images. Extensive quantitative and qualitative
evaluations on benchmarks demonstrate that OPEC-Net achieves significant
improvements over recent leading works. Notably, our OCPose is the most complex
occlusion dataset with respect to average IoU between adjacent instances.
Source code and OCPose will be publicly available.
