Reconstructing 3D humans and scenes from monocular videos is a challenging task, particularly due to human motion, varying illumination, and dynamic scene shadows. While recent works have explored scene disentanglement by jointly modeling humans and their surrounding scenes, they often overlook illumination and shadow effects--resulting in inconsistent human appearance and degraded scene realism. To address this gap, we propose a photometrically consistent integration of human and scene reconstruction based on 3D Gaussian Splatting, with a key focus on modeling spatially-varying illumination and shadows. Central to our method is a learnable light volume that provides localized lighting cues to human Gaussians, enabling more realistic and consistent appearance synthesis. To further ensure accurate human geometry and alignment, we adopt a two-stage reconstruction strategy: we first optimize a human mesh and then anchor Gaussians to the refined surface. In addition, we introduce an implicit shadow estimation module that disentangles cast shadows from the scene, thus supporting plausible human shadow synthesis. Our framework also facilitates human relighting and compositing into novel scenes with contextually appropriate lighting. Quantitative and qualitative results demonstrate that our method achieves state-of-the-art performance, producing consistent appearances, realistic illumination, and enhanced overall scene realism.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Applying 3D Gaussian Splatting to inverse rendering, especially for relightable assets under high-illuminance conditions, remains challenging. Strong specular highlights and complex reflections complicate material-light disentanglement, often baking in shadows and losing specular detail. To address this, we introduce IR-HGP, a framework that achieves robust disentanglement using three synergistic modules: First, a Hybrid Visibility Decomposition module ensures physical visibility consistency. Second, a Generative Illumination Field Prior module infers detailed and high-dynamic range environmental lighting. Finally, a Physics-Aware Radiance Correction module stabilizes optimization and mitigates illumination artifacts. Our framework achieves SOTA material recovery and relighting performance, outperforming existing methods under challenging illumination conditions. It reconstructs the view-dependent "shiny" appearance of reflective surfaces in real time, surpassing the limits of prior 3DGS-based inverse rendering methods.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表