Luce: Relightable Gaussians for 3D Asset Generation

High-fidelity image-to-3D generation requires a 3D representation that captures
both geometry and appearance. To support relighting and integration into standard
rendering pipelines, the representation should include physically based rendering
(PBR) modalities such as albedo, metallic-roughness, and surface normals. We
propose Luce, a 3D representation that unifies geometry and PBR materials within
a voxelized multimodal Gaussian cloud, using dedicated Gaussian primitives for
each modality. A variational autoencoder compresses this representation into a
unified material-aware latent space. A rectified-flow transformer generates this la-
tent from a single image, conditioned on multi-layer features from a pretrained im-
age encoder that preserve both semantic context and fine spatial detail. The latent
then decodes into relightable PBR Gaussians and an optional textured mesh with
a tangent-space normal map. On Toys4K, Luce achieves state-of-the-art single-
image-to-3D generation, improving FID by 28% over the strongest baseline. We
further introduce a benchmark of AI-generated images, on which Luce improves
the CLIP image-alignment score over the best baseline (0.8519 vs. 0.8299). Luce
generates relightable, geometrically accurate, and materially faithful assets that
preserve fine details such as text, logos, and inscriptions.