The　ongoing　accumulation　of　radio-echo　sounding　(RES)　measurements　in　Antarctica　in　recent　years　has　significantly　expanded　our　understanding　of　subglacial　structures.　The　effective　use　of　RES-collected　data　construct　accurate　Antarctic　subglacial　topography　has　emerged　as　a　vital　component　of　contemporary　polar　research.　Various　methods,　including　conventional　interpolation,　inversion　techniques,　and　even　deep　learning　methods,　have　been　used　to　recreate　Antarctic　bed　topography.　However,　these　bed　topographies　are　often　plagued　by　over-smoothing,　loss　of　small-scale　features,　low　precision,　and　instability.　The　Siamese　topographic　generation　model　(STGM)　is　proposed　here　to　address　the　above　mentioned　issues.　After　being　trained　on　ArcticDEM,　this　model　can　generate　Antarctic　subglacial　topography　with　stability　and　accuracy　by　merging　the　advantages　of　deep　learning-based　generative　models,　Siamese　networks,　kernel　prediction,　and　deformable　convolutions.　In　terms　of　evaluation,　both　quantitative　and　qualitative　comparisons　with　current　Antarctic　subglacial　digital　elevation　models　demonstrate　that　our　method　can　generate　topographical　features,　such　as　mountains,　ice　streams,　and　valleys,　with　high　precision　and　minimal　artifacts.　In　quantitative　validation,　our　model　achieves　over　20%　improvement　in　both　Mean　Absolute　Error　(MAE)　and　Root　Mean　Square　Error　(RMSE)　compared　to　the　previously　best-performing　method　(GEI),　surpassing　existing　models　in　terms　of　accuracy　and　detail.　Moreover,　an　error　analysis　specifically　focusing　on　the　effect　of　varying　track　intervals　has　been　conducted,　offering　a　benchmark　for　future　investigations　into　the　influence　of　track　density　on　model　errors.　Finally,　using　STGM　based　on　the　RES　data,　the　subglacial　topography　of　Princess　Elizabeth　Land　has　also　been　successfully　generated.　In　this　area,　the　topography　generated　by　STGM　at　a　resolution　of　500　m　clearly　depicts　subglacial　lakes　and　valleys,　revealing　the　complexity　and　diversity　of　the　subglacial　topography.