ing at the problem of inaccurate estimation results caused by the complexity of limbs and environment in human pose estimation, a human pose estimation method based on secondary generation adversary is proposed in this work. The stacked hourglass network (SHN) is trained for generation adversary through two stages. First, the SHN is used as a discriminator in the first generation adversarial network model, and the on-line adversarial data is used to strengthen training to improve the estimation performance of the SHN. Then, the SHN acts as a generator in the second generation adversarial network model, and the limb geometric constraints are used as the discriminator. The estimation performance of the SHN is improved again through the second adversarial training, and the final SHN is obtained. The proposed method is tested on the public data sets LSP and MPII, and the results show that it can effectively improve the estimation accuracy of the SHN.