Though　many　methods　have　been　proposed,　image　completion　still　remains　challenge;　besides　textured　patterns　completion,　it　often　requires　high-level　understanding　of　scenes　and　objects　being　completed.　More　recently,　deep　convolutional　generative　adversarial　networks　have　been　turned　into　an　efficient　tool　for　image　completion.　Manually　specified　transformation　methods　are　having　been　replaced　with　training　neural　nets.　Hand-engineered　loss　calculations　for　training　the　generator　are　replaced　by　the　loss　function　provided　by　the　discriminator.　With　existing　deep　learning-based　approaches,　image　completion　results　in　high　quality　but　may　still　lack　high-level　feature　details　or　contain　artificial　appearance.　In　our　completion　architecture,　we　leverage　a　fully　convolutional　generator　with　two　subnetworks　as　our　basic　completion　approach　and　divide　the　problem　into　two　steps:　The　first　subnetwork　generates　the　outline　of　a　completed　image　in　a　new　domain,　and　the　second　subnetwork　translates　the　outline　to　a　visually　realistic　output　with　image-to-image　translation.　The　feedforward　fully　convolutional　network　can　complete　images　with　holes　of　any　size　at　any　location.　We　compare　our　method　with　several　existing　ones　on　representative　datasets　such　as　CelebA,　ImageNet,　Places2　and　CMP　Facade.　The　evaluations　demonstrate　that　our　model　significantly　improves　the　completion　results.