Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy of visible joints when some joints are invisible. The network consists of two simple but efficient modules, i.e., Cascade Feature Network (CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction maps from the previous stages to guide the prediction maps in the next stage to produce accurate human pose. Second, the GSN is designed to contribute to the localization of invisible joints by passing message among different joints. According to GAN, if the prediction pose produced by the generator $G$ cannot be distinguished by the discriminator $D,$ the generator network $G$ has successfully obtained the underlying dependence of human joints. We conduct experiments on three widely used human pose estimation benchmark datasets, i.e., LSP, MPII and COCO, whose results show the effectiveness of our proposed framework.

由于照明，遮挡和重叠导致的图像中看不见的人体关键点，因此对于大多数当前的人体姿势估计方法而言，很可能产生不合理的人体姿势预测。在本文中，我们设计了一种新型的生成对抗网络（GAN），以在某些关节不可见时提高可见关节的定位精度。网络由两个简单但有效的模块组成，即，级联特征网络（CFN）和图结构网络（GSN）。首先，CFN利用前一阶段的预测图来指导下一阶段的预测图，以产生准确的人体姿势。其次，GSN旨在通过在不同关节之间传递信息来促进不可见关节的定位。根据GAN，如果生成器生成了预测姿势$G$ 区分者无法区分 $d，$ 发电机网络 $G$已成功获得人体关节的潜在依赖性。我们对三种广泛使用的人体姿势估计基准数据集进行了实验，即LSP，MPII和COCO，其结果表明了我们提出的框架的有效性。