Gait recognition has been proved useful in human identification at a distance. But view variance of gait feature is always a great challenge because of the difference in appearance. If the view of the probe is different from that of the gallery, one view transformation model can be employed to convert the gait feature from one view to another. But most existing models need to estimate the view angle first, and can work for only one view pair. They can not convert multi-view data to one specific view efficiently. We employ one deep model based on auto-encoder for view invariant gait extraction. The model can synthesize gait feature in a progressive way by stacked multi-layer auto-encoders. The unique advantage is that it can extract view invariant feature from any view using only one model, and view estimation is not needed. The proposed method is evaluated on a large dataset, CASIA Gait Dataset B. The experimental results show that it can achieve state-of-the-art performance, and the improvement is more obvious when the view variance is larger.