# Luce: Relightable Gaussians for 3D Asset Generation

DevFeed: [Luce: Relightable Gaussians for 3D Asset Generation](<https://devfeed.tech/articles/luce-relightable-gaussians-for-3d-asset-generation-6733.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/relightable-gaussians-3d-generation>)

Published: 2026-08-26T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [generation](<https://devfeed.tech/tags/generation.md>), [images](<https://devfeed.tech/tags/images.md>), [mesh](<https://devfeed.tech/tags/mesh.md>), [models](<https://devfeed.tech/tags/models.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [vision](<https://devfeed.tech/tags/vision.md>)

## AI overview

Luce is a multimodal 3D representation for generating relightable assets from a single image. It combines geometry with physically based materials in a voxelized Gaussian cloud, compresses them into a material-aware latent space, and generates relightable PBR Gaussians and optional textured meshes. On Toys4K, it reports a 28% FID improvement over the strongest baseline and improves alignment on an AI-generated image benchmark.

## Source excerpt

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...