Abstract
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 this https URL.
Abstract (translated)
在3D人体姿态估计（HPE）领域，准确估计人体姿态，尤其是在遮挡情况下，是一个重要的挑战。这项工作识别并解决了当前3D HPE领域关于数据稀缺性和处理遮挡策略的不足。我们引入了我们的新BlendMimic3D数据集，旨在模拟真实世界场景中遮挡发生的情况，实现无缝集成到3D HPE算法中。此外，我们提出了一个3D姿态优化模块，通过图卷积网络（GCN）增强姿态表示。这个GCN模块是一个可插拔的解决方案，适用于各种3D HPE框架，无需重新训练它们。通过用BlendMimic3D中的遮挡数据训练GCN，我们证明了在解决遮挡姿态方面取得了显著的改进，与未遮挡的姿态具有可比较的结果。项目网页链接为https://www.example.com。
URL
https://arxiv.org/abs/2404.16136
