HyGI: Neural Global Illumination for Dynamic Hybrid Gaussian-Mesh Scenes

Relighting dynamic scenes composed of 2D Gaussians and triangle meshes requires computing global illumination, i.e., light transport between these hybrid assets, to produce plausible shading, shadows, and indirect illumination effects.Existing relightable Gaussian methods primarily address relighting individual Gaussian assets, rather than modeling dynamic light transport between Gaussians and triangle meshes. Although a few real‑time global illumination techniques target such hybrid scenes, they often introduce severe visual artifacts due to the heuristic approximations required by vanilla real‑time renderer. Instead, we present HyGI, a neural global illumination renderer for hybrid Gaussian–mesh scenes with dynamic objects, lights, and views. Given a scene's Gaussian and mesh assets with physically based materials, HyGI takes an inexpensive stochastic G-buffer and light-space shadow maps as scene and shadow cues, then aggregates these spatially coherent information for efficient relighting with a kernel-predicting layer guided by Gaussian footprint descriptors, and further end-to-end predicts direct shading, visibility, and indirect illumination with light encoders and separate decoders. Our method targets interactive light transport between Gaussian and mesh assets and successfully produces the high visual quality of physically-based rendering in real-time. Across twelve scenes, HyGI improves valid-surface PSNR by 1.8–9.0dB over the real-time hybrid renderer 3DGS-GI and by 2.6–11.2dB over relightable Gaussian methods on static scenes, at 29–59ms per frame.