تقرير
3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement
| العنوان: | 3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement |
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| المؤلفون: | Lino, Filipa, Santiago, Carlos, Marques, Manuel |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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/. Comment: Accepted at 6th Workshop and Competition on Affective Behavior Analysis in-the-wild - CVPR 2024 Workshop |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.16136 |
| رقم الأكسشن: | edsarx.2404.16136 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |