Understanding　subglacial　bed　topography　is　essential　for　learning　about　Antarctica　in　the　geologic　and　glaciological　fields.　The　primary　method　of　investigating　the　Antarctic　bed　involves　measuring　the　bed　elevation　by　radio-echo　sounding　(RES)　deployed　on　aircraft.　Digital　elevation　models　(DEMs)　of　the　Antarctic　bed　generated　by　traditional　interpolation　methods　usually　lack　resolution,　precision,　and　roughness.　To　generate　Antarctic　bed　DEMs　by　interpolating　sparse　RES　bed　elevation　data,　we　use　a　two-stage　coarse-to-fine　fully　convolutional　neural　network　(CNN),　which　presents　a　deep　generative　elevation　inpainting　method　that　can　extract,　use　in-depth　features,　and　reconstruct　the　bed　elevation　conforming　to　the　textural　character　of　deglacial　landscapes.　Our　method　can　generate　a　detailed　and　reasonable　bed　DEM　with　the　full　calculation　of　CNN　and　the　training　strategy　of　a　generative　adversarial　network　(GAN).　The　quantitative　evaluation　results　show　that　a　250-m　resolution　elevation　grid　map　with　a　77-m　mean　absolute　error　(MAE)　can　be　generated　through　elevation　inpainting　by　sparse　data　with　4-km　RES　survey　spacing　in　the　Arctic　test　area.　Our　study　also　generates　two　realistic　bed　DEMs　with　a　250-m　spatial　resolution　in　the　Gamburtsev　Subglacial　Mountains　and　Amundsen　Sea　Embayment.　Compared　with　the　existing　Antarctic　bed　DEM　products,　BedMachine_Antarctica,　DeepBedMap_DEM,　and　MB_DeepBedMap_DEM,　our　generated　bed　DEMs　show　more　realistic　terrain　and　elevation　with　low　MAEs　in　test　regions,　which　could　better　suit　follow-up　glaciological　research.　The　code　of　this　work　will　be　available　at　https://github.com/Hecian/GEI_2022　for　the　sake　of　reproducibility.