Luce: Relightable Gaussians for 3D Asset Generation

arXiv:2608.23943 · cs.CV, cs.AI, cs.GR · Submitted 2026-08-25 · Read on arXiv

cs.CV, cs.AI, cs.GR

Submitted: 2026-08-25

Updated: 2026-09-28

Comments: 28 pages, 19 figures, 5 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 16× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Decode — 9× in this paper · explained in 2 episodes
• Gaussian Splatting — 9× in this paper · explained in 4 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Fidelity — 6× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 5× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Anisotropy — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• LiTo: Surface Light Field Tokenization
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Shap-E: Generating Conditional 3D Implicit Functions
• GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• MatSpray: Fusing 2D Material World Knowledge on 3D Geometry
• Structured 3D Latents for Scalable and Versatile 3D Generation
• Native and Compact Structured Latents for 3D Generation
• Large Material Gaussian Model for Relightable 3D Generation
• RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
• TexVerse: A Universe of 3D Objects with High-Resolution Textures

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