CAGE-GS: Camera-Aware Geometry Enhancement for Generalizable Sparse-View Human Gaussian Splatting

Sparse-view observations provide limited geometric evidence for generalizable human reconstruction, making reliable cross-view correspondence and view-consistent Gaussian prediction difficult. Existing methods either depend on parametric human templates or rely on depth estimates without sufficient cross-view evidence, leading to incomplete or spatially inconsistent geometry and unreliable cross-view aggregation. We present CAGE-GS, a generalizable framework that progressively transforms coarse geometry into reliable cross-view evidence for Gaussian reconstruction. Specifically, a Camera-Aware Warp Refiner establishes camera-grounded correspondence from coarse inverse depth and uses cross-view semantic consistency to guide iterative geometry refinement. The refined geometry then guides a Projection-Aware Cross-View Attention to construct spatially consistent cross-view candidates and selectively aggregate complementary multi-view features for Gaussian prediction. Together, these components turn coarse geometry into reliable camera-grounded evidence for geometry-consistent Gaussian prediction from sparse calibrated views, without relying on parametric human templates or ground-truth depth. Across three benchmarks under the four-view setting, CAGE-GS improves PSNR and SSIM by up to 3.34 dB and 0.0164, respectively, while reducing LPIPS by up to 22.8%, with consistent advantages across varying input-view configurations.