Mat2GS: Feed-Forward Relighting with Explicit Materials on 2D Gaussians

Relighting is essential for seamlessly integrating 3D assets into diverse environments, enabling applications such as augmented reality, simulation, and digital content creation. However, existing relighting methods often rely on implicit appearance modeling or image-space material prediction coupled with deferred shading, and struggle to faithfully reproduce specular highlights and shadows under novel illumination. To mitigate this issue, we propose a feed-forward relighting framework based on 2D Gaussian Splatting (2DGS) that directly attaches explicit material attributes to 2D Gaussians, establishing a unified representation of geometry and materials. Through accurate geometry and material prediction, our method produces realistic specular highlights and shadows under novel environment lighting. We further construct a large-scale synthetic dataset by rendering a nearly 110K object subset from Objaverse, providing images with various viewpoints and illumination under corresponding material and geometry. Experiments demonstrate that our method outperforms existing open-source state-of-the-art approaches in relighting quality.