Motion during MRI scan can reduce image quality due to the induced artifacts. We present a novel data-driven motion correction method for magnitude MR images using generative adversarial networks (GANs). GANs (Pix2pix model) is implemented to reduce motion artifacts and reconstruct motion-corrupted images through adversarial training between generator and discriminator to force motion-corrected image close to the reference image. The training set is made of image pairs, which consist of motionless reference images and corresponding motion-simulated images. The proposed method was validated by a simulated motion test set and a real motion (experimental) test set.