In order to improve the detection accuracy of Visible-Infrared Cross-modal Person re-identification, a dual flow network model based on Visible-Infrared Cross-modal bidirectional feature generation is proposed. Compared with the existing algorithms, employ the bidirectional feature generation method to transfer the Cross-modal pedestrian features, which significantly enhances the Cross-modal feature expression. At the same time, the dual flow network is used to extract the discriminative dual-modal features, and the coarse-grained and fine-grained loss fusion strategy designed improves the accuracy of cross-modal pedestrian retrieval.The experimental results indicate that compared with the latest method, the average accuracy of cross modal pedestrian recognition, 92.91% on RegDB and 66.17% on SYSU-MM01, is increased effectively by proposed method.