Recently, tremendous progress has been achieved on human pose estimation with the development of convolutional neural networks (CNNs). However, current methods still suffer from severe occlusion, back view, and large pose variation due to the lack of consideration of the spatial relationship between different joints, which can provide strong cues for localizing the hidden keypoints. In this work, we design a Structural Pose Network (SPN) to take full advantage of joint structure for human pose estimation under unconstrained environment. Specifically, the proposed model is composed of two subnets: Structure Residual Network (SRN) and Structure Improving Network (SIN). Given an input image, SRN first captures rich joint structure as priors through a multi-branch feature extraction module, following a hourglass network with pyramid residual units to enlarge the receptive field and further obtain structural feature representations. SIN, based on coordinate regression, can optimize the spatial relationship of different joints via the attention mechanism, thus refining the initial prediction from SRN. In addition, we propose a novel structure-consistency constraint, which can maintain the structural consistency between the joints and body parts via estimating whether the joints are located in their corresponding parts. At the same time, an online hard regions mining (OHRM) strategy is introduced to drive the network to pay corresponding attention to different body parts. The experimental results on three challenging datasets show that our method outperforms other state-of-the-art algorithms.

中文翻译：

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学习人体姿态估计的联合结构

最近，随着卷积神经网络 (CNN) 的发展，人体姿态估计取得了巨大进展。然而，由于没有考虑不同关节之间的空间关系，目前的方法仍然存在严重的遮挡、后视和较大的姿态变化，这可以为定位隐藏的关键点提供强有力的线索。在这项工作中，我们设计了一个结构姿势网络（SPN），以充分利用关节结构在无约束环境下进行人体姿势估计。具体来说，所提出的模型由两个子网组成：结构残差网络（SRN）和结构改进网络（SIN）。给定输入图像，SRN 首先通过多分支特征提取模块捕获丰富的联合结构作为先验，遵循带有金字塔残差单元的沙漏网络来扩大感受野并进一步获得结构特征表示。SIN 基于坐标回归，可以通过注意力机制优化不同关节的空间关系，从而细化 SRN 的初始预测。此外，我们提出了一种新的结构一致性约束，它可以通过估计关节是否位于其相应部位来保持关节和身体部位之间的结构一致性。同时，一个 我们提出了一种新颖的结构一致性约束，它可以通过估计关节是否位于其相应部位来保持关节和身体部位之间的结构一致性。同时，一个 我们提出了一种新颖的结构一致性约束，它可以通过估计关节是否位于其相应部位来保持关节和身体部位之间的结构一致性。同时，一个在线困难地区挖掘（OHRM）策略被引入来驱动网络对不同的身体部位给予相应的关注。在三个具有挑战性的数据集上的实验结果表明，我们的方法优于其他最先进的算法。