Portrait inpainting was widely used in the photo editing based on image rendering and computational photography. A lot of factors including the variety in clothing, different body types such as tall, short, fat and thin size, the high freedom degree of human body pose, bring difficulties to portrait inpainting. Therefore, an efficient portrait inpainting method based on Generating Adversarial Network (GAN) was proposed. The algorithm consists two stages. During the first stage, the image was roughly inpainted based on an encoder-decoder network, and then the body pose information in the image was estimated. During the second stage, the portrait was accurately inpainted based on the pose information and GAN. Besides, the key points of the portrait pose were connected by using portrait pose information to form the pose framework and perform the dilation operation, and the portrait pose mask was obtained. Thereby, a portrait pose loss function was constructed for network training. The experimental results show that: compared with the Contextual Attention inpainting method, the proposed method has the SSIM （Structural SIMilarity index） increased by one percentage point. The method, by adding the portrait pose information into the portrait inpainting process, effectively constrains the solution space range of portrait data in the zone to be inpainted, and strengthens the network's attention to the portrait pose information.