Unpaired image-to-image translation is a tricky task which aims at learning a mapping from one image collection to another image collection without any pair-labeled information. Recent works have proposed cycle-consistency assumption to deal with this task. However, the result is still unsatisfactory for geometric translation. To address this limitation, this paper proposes a novel method using shared deep space generative adversarial network (SDSGAN). Both two images are encoded into a shared deep space through a pre-trained VGG-19 network, and then we use two decoders to convert them separately to corresponding image domains. In addition, we introduce skip-connection block and self-reconstruction loss to facilitate the mapping. Experimental results show that the proposed SDSGAN has both numerical and perceptual superiorities to existing methods.