mlsanityData to 7 Oct 2026

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

arXiv 2404.16136 ↗First posted 24 Apr 2024cs.CVcited by 2

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Abstract (arXiv)

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/.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 44 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

31 of 44 references.

References · 31

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19 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Benchmarking 3D Human Pose Estimation Models under Occlusionscited by 2
  2. 2025Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activitiescited by 0