SceneShine: Illumination-aware Human Scene Gaussian Re-Splatting from Mobile Device Video

Abstract

Standard 3DGS falls short in precise relighting and shadowing needed to realistically integrate humans into novel environments. We bridge this gap with SceneShine an illumination-aware framework designed for seamless composition through physically-based avatar relighting and shadow casting. Relighting human surfaces in in-the-wild videos is inherently ill-posed, often making the simultaneous disentanglement of scene lighting and BRDF properties difficult. We overcome this ambiguity by utilizing a pseudo-global light map prior to guide BRDF parameter decomposition, significantly reducing relighting artifacts. Additionally, we implement point-based ray tracing to manage human-scene occlusions and dynamically update scene colors for accurate shadow casting. We also introduce a new synthetic dataset for evaluation. Extensive experiments show that our method surpasses existing approaches in reconstruction fidelity and identity preservation while achieving highly convincing illumination-aware integration.