Benchmarking 3D Human Pose Estimation Models Under Occlusions

This paper addresses critical challenges in 3D Human Pose Estimation (HPE) by analyzing the robustness and sensitivity of existing models to occlusions, camera position, and action variability. Using a novel synthetic dataset, BlendMimic3D, which includes diverse scenarios with multi-camera setups and several occlusion types, we conduct specific tests on several state-of-the-art models. Our study focuses on the discrepancy in keypoint formats between common datasets such as Human3.6M, and 2D datasets such as COCO, commonly used for 2D detection models and frequently input of 3D HPE models. Our work explores the impact of occlusions on model performance and the generality of models trained exclusively under standard conditions. The findings suggest significant sensitivity to occlusions and camera settings, revealing a need for models that better adapt to real-world variability and occlusion scenarios. This research contributed to ongoing efforts to improve the fidelity and applicability of 3D HPE systems in complex environments.

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

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/.