可泛化和可重照明的高斯点云法用于人体新视角合成
Generalizable and Relightable Gaussian Splatting for Human Novel View Synthesis
AI总结：
GRGS通过可泛化和可重照明的3D高斯框架，实现了在多光照条件下高保真的人体新视角合成，结合物理基础的神经渲染和光照鲁棒的几何细化模块，提升了视觉质量和泛化能力。
摘要：
我们提出GRGS，一种可泛化和可重照明的3D高斯框架，用于在多样的光照条件下实现高保真的人体新视角合成。与现有方法依赖于每个性别优化或忽略物理约束不同，GRGS采用前馈、全监督策略，将多视角2D观测中的几何、材质和光照线索投影到3D高斯表示中。为了在多样的光照条件下恢复准确的几何结构，我们引入了一个光照鲁棒的几何细化（LGR）模块，该模块在合成重照明数据上进行训练，以预测精确的深度和表面法线。基于高质量的几何结构，进一步提出了一个物理基础的神经渲染（PGNR）模块，以将神经预测与基于物理的阴影相结合，支持可编辑的重照明，包括阴影和间接照明。此外，我们设计了一种2D到3D的投影训练方案，利用可微监督的环境遮挡、直接和间接光照图，减轻了光线追踪的计算成本。大量实验表明，GRGS在视觉质量、几何一致性以及跨人物和光照条件的泛化能力方面均优于现有方法。
英文摘要：
We propose GRGS, a generalizable and relightable 3D Gaussian framework for high-fidelity human novel view synthesis under diverse lighting conditions. Unlike existing methods that rely on per-character optimization or ignore physical constraints, GRGS adopts a feed-forward, fully supervised strategy projecting geometry, material, and illumination cues from multi-view 2D observations into 3D Gaussian representations. To recover accurate geometry under diverse lighting conditions, we introduce a Lighting-robust Geometry Refinement (LGR) module trained on synthetically relit data to predict precise depth and surface normals. Based on the high-quality geometry, a Physically Grounded Neural Rendering (PGNR) module is further proposed to integrate neural prediction with physics-based shading, supporting editable relighting with shadows and indirect illumination. Moreover, we design a 2D-to-3D projection training scheme leveraging differentiable supervision from ambient occlusion, direct, and indirect lighting maps, alleviating the computational cost of ray tracing. Extensive experiments demonstrate that GRGS achieves superior visual quality, geometric consistency, and generalization across characters and lighting conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
