Motion is one of the main sources for artifacts in magnetic resonance (MR) imaging and can affect the diagnostic quality of MR images significantly. Previously, supervised adversarial approaches have been suggested for the correction of MR motion artifacts. However,supervised approaches require paired and co-registered datasets for training, which are often hard or impossible to acquire. We introduced a new adversarial framework for the unsupervised correction of severe rigid motion artifacts in the brain region. Quantitative and qualitative comparisons with other supervised and unsupervised translation approaches showed the enhanced performance of the introduced framework.