Game2GS: Coverage-Aware Gaussianization for Large-Scale Free-View HDR Rendering

Modern game worlds combine detailed geometry, materials, lighting, and engine-specific rendering effects, making their visual appearance costly to transfer across platforms. Distilling this appearance into 3D Gaussian representations offers a way to reuse game assets beyond the source engine, but requires faithful 6DoF rendering and exposure adaptation under complex lighting. Existing large-scale reconstruction pipelines are typically trained on images captured along prescribed street trajectories. Denser sampling along these trajectories increases acquisition costs without necessarily providing sufficiently resolved and directionally diverse observations of all relevant surfaces. This motivates selecting more informative views under a limited image budget. These views must also capture the world's bright and dark regions on a consistent radiometric scale to support runtime exposure adaptation. However, auto-exposed LDR capture introduces inconsistent brightness supervision across views, whereas fixed-exposure LDR capture can lose highlight or shadow detail. We introduce Game2GS, a framework combining coverage-aware view acquisition with HDR Gaussian reconstruction. Using known source geometry, we select camera poses derived from baked lighting probes to improve surface coverage through well-resolved observations from complementary directions and distinct positions. We then reconstruct an HDR Gaussian scene from fixed-exposure captures and enable real-time automatic exposure using an engine-exported compensation curve. Under matched image budgets, Game2GS improves PSNR by more than 3 dB over the strongest prescribed-view baseline across all evaluated scenes on independent 6DoF test views, and we will release our private game scenes.