Aiming at the problems of small sample data and single feature of the person data collected in thefield of visual surveillance, a method of generating a desired person pose image with a bidirectional generative ad.versarial network with high visual perception constraints is proposed. A single image of a given character and atwo-dimensional skeleton of a desired pose are used as inputs to a bidirectional generation adversarial network togenerate an image with the desired pose of the target person. The generated expected pose image is mapped back tothe original pose image, and a small number of images are used for learning in an unsupervised learning manner togenerate a high-quality image of the character′s desired pose. The proposed method is tested on the DeepFashionpublic data set. The results show that the image structure similarity (SSIM) generated by the method is 0.28 higherthan that of previous methods, which effectively improves the image generation quality of single-personmulti-pose based on unsupervised learning.