关键词: 卷积神经网络, 生成对抗网络（GAN）, 人体姿态识别, 堆叠沙漏网络, 层次感知

Abstract:

Aiming at the problem of accurately locating some important joint points in human body modeling, a novel deep convolution generative adversarial network is proposed to estimate human poses from still images. Specifically, a stacked hourglass network with deep convolution network is used to accurately locate the key joint points in the images, the generative and the discriminative portions of the proposed adversarial deep neural network are designed to encode the spatial relationship between the parts in the first stage of the hierarchy（parents） and the parts in the second stage of the hierarchy（children）. Each of the generator and the discriminator networks is designed as two components, which are sequentially connected together to infer rich appearance potentials and to encode not only the likelihood of the part’s existence but also the relationships between each body part and its parent. Finally, in the static images, the key nodes of the human body model and the approximate human poses can be estimated accurately. The method is tested on different data sets, in most cases, the results of the proposed method are better than those of other methods.

Key words: convolutional neural network, Generative Adversarial Network（GAN）, human pose estimation, stacked hourglass network, hierarchy-aware