Recent 3D-aware head generative models based on 3D Gaussian Splatting achieve real-time, photorealistic and view-consistent head synthesis. However, a fundamental limitation persists: the deep entanglement of illumination and intrinsic appearance prevents controllable relighting. Existing disentanglement methods rely on strong assumptions to enable weakly supervised learning, which restricts their capacity for complex illumination. To address this challenge, we introduce HeadLighter, a novel supervised framework that learns a physically plausible decomposition of appearance and illumination in head generative models. Specifically, we design a dual-branch architecture that separately models lighting-invariant head attributes and physically grounded rendering components. A progressive disentanglement training is employed to gradually inject head appearance priors into the generative architecture, supervised by multi-view images captured under controlled light conditions with a light stage setup. We further introduce a distillation strategy to generate high-quality normals for realistic rendering. Experiments demonstrate that our method preserves high-quality generation and real-time rendering, while simultaneously supporting explicit lighting and viewpoint editing. We will publicly release our code and dataset.

翻译：近年来，基于3D高斯泼溅的3D感知头部生成模型已实现实时、逼真且视角一致的头部合成。然而，一个根本性局限依然存在：光照与内在外观的深度纠缠阻碍了可控的重光照操作。现有的解耦方法依赖强假设以实现弱监督学习，这限制了其处理复杂光照的能力。为应对这一挑战，我们提出了HeadLighter——一种新颖的监督式框架，旨在头部生成模型中学习外观与光照的物理可信分解。具体而言，我们设计了一种双分支架构，分别建模光照无关的头部属性与基于物理的渲染组件。通过渐进式解耦训练，在光阶装置捕获的受控光照条件下多视角图像的监督下，逐步将头部外观先验注入生成式架构。我们进一步引入一种蒸馏策略以生成高质量法线，实现逼真渲染。实验表明，我们的方法在保持高质量生成与实时渲染的同时，支持显式的光照与视角编辑。我们将公开代码与数据集。