3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

Filipa Lino, Carlos Santiago, Manuel Marques; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4646-4656

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/ .