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, 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, LSP, MPII and COCO, whose results show the effectiveness of our proposed framework.

翻译：由于光化、隔离和重叠造成的图像中隐形的人类关键点,很可能对目前人类构成的多数估计方法产生不合理的人类构成预测。在本文件中,我们设计了一个新型的基因对抗网络(GAN),以便在一些联合是无形的时,提高可见连接的本地化准确性。该网络由两个简单而有效的模块组成:Cascade 地貌网络和图象结构网络(GSN)。首先,CFN利用前几个阶段的预测地图来指导下一个阶段的预测地图,以产生准确的人类构成。第二,GSN的目的是通过在不同联合之间传递信息,促进无形连接的本地化。据GAN称,如果G生成的预测无法被歧视者D区分出来,G生成的预测就成功地获得了人类联合的基本依赖性。我们对三种广泛使用的人类构成基准数据集(LSP、MPII和COCO)进行了实验,其结果显示了我们提议的框架的有效性。