  In the field of 3D Human Pose Estimation (HPE), accurately estimating human
pose, especially in scenarios with occlusions, is a significant challenge. This
work identifies and addresses a gap in the current state of the art in 3D HPE
concerning the scarcity of data and strategies for handling occlusions. We
introduce our novel BlendMimic3D dataset, designed to mimic real-world
situations where occlusions occur for seamless integration in 3D HPE
algorithms. Additionally, we propose a 3D pose refinement block, employing a
Graph Convolutional Network (GCN) to enhance pose representation through a
graph model. This GCN block acts as a plug-and-play solution, adaptable to
various 3D HPE frameworks without requiring retraining them. By training the
GCN with occluded data from BlendMimic3D, we demonstrate significant
improvements in resolving occluded poses, with comparable results for
non-occluded ones. Project web page is available at
https://blendmimic3d.github.io/BlendMimic3D/.
