  Decoupling the illumination in 3D scenes is crucial for novel view synthesis
and relighting. In this paper, we propose a novel method for representing a
scene illuminated by a point light using a set of relightable 3D Gaussian
points. Inspired by the Blinn-Phong model, our approach decomposes the scene
into ambient, diffuse, and specular components, enabling the synthesis of
realistic lighting effects. To facilitate the decomposition of geometric
information independent of lighting conditions, we introduce a novel bilevel
optimization-based meta-learning framework. The fundamental idea is to view the
rendering tasks under various lighting positions as a multi-task learning
problem, which our meta-learning approach effectively addresses by generalizing
the learned Gaussian geometries not only across different viewpoints but also
across diverse light positions. Experimental results demonstrate the
effectiveness of our approach in terms of training efficiency and rendering
quality compared to existing methods for free-viewpoint relighting.
