3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement
It addresses a gap in 3D human pose estimation for applications like robotics and AR/VR by providing a dataset and method to handle occlusions, though it appears incremental as it builds on existing frameworks.
This paper tackles the challenge of accurately estimating 3D human pose in occluded scenarios by introducing the BlendMimic3D dataset and a GCN-based refinement block, demonstrating significant improvements in resolving occluded poses with comparable results for non-occluded ones.
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/.