In this paper, we study the problem of monocular 3D human pose estimation based on deep learning. Due to single view limitations, the monocular human pose estimation cannot avoid the inherent occlusion problem. The common methods use the multi-view based 3D pose estimation method to solve this problem. However, single-view images cannot be used directly in multi-view methods, which greatly limits practical applications. To address the above-mentioned issues, we propose a novel end-to-end 3D pose estimation network for monocular 3D human pose estimation. First, we propose a multi-view pose generator to predict multi-view 2D poses from the 2D poses in a single view. Secondly, we propose a simple but effective data augmentation method for generating multi-view 2D pose annotations, on account of the existing datasets (e.g., Human3.6M, etc.) not containing a large number of 2D pose annotations in different views. Thirdly, we employ graph convolutional network to infer a 3D pose from multi-view 2D poses. From experiments conducted on public datasets, the results have verified the effectiveness of our method. Furthermore, the ablation studies show that our method improved the performance of existing 3D pose estimation networks.

中文翻译：

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基于深度学习的多视角姿态生成器，用于单目 3D 人体姿态估计

在本文中，我们研究了基于深度学习的单眼 3D 人体姿态估计问题。由于单视图的限制，单目人体姿态估计无法避免固有的遮挡问题。常用的方法是使用基于多视图的 3D 姿态估计方法来解决这个问题。然而，单视图图像不能直接用于多视图方法，这极大地限制了实际应用。为了解决上述问题，我们提出了一种新颖的端到端 3D 姿态估计网络，用于单目 3D 人体姿态估计。首先，我们提出了一个多视图姿态生成器，用于从单个视图中的 2D 姿态预测多视图 2D 姿态。其次，考虑到现有数据集（例如 Human3.6M 等），我们提出了一种简单但有效的数据增强方法，用于生成多视图 2D 姿态注释。) 在不同视图中不包含大量 2D 姿势注释。第三，我们使用图卷积网络从多视图 2D 姿态推断 3D 姿态。从在公共数据集上进行的实验来看，结果验证了我们方法的有效性。此外，消融研究表明，我们的方法提高了现有 3D 姿态估计网络的性能。