Updated 2 h ago · first seen 11 Sept 2026
paper_01M294AHN5Y9Z139HVSQVE5DTQ
- Published
- 26 Aug 2026
- T1 · 2 h ago
- arXiv
- 2608.23943
- T1 · 2 h ago
Abstract
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…
Authors 10
Mayank Singh, Michele Stoppa, Alvise Memo, Rui Yu, Sree Harsha Kalli, Srimanth Gunturi, Muhammad Ahmed Riaz, Behrooz Shahsavari, Waleed Abdulla, David E. Jacobs
Specification
- Paper
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- arXiv id
- 2608.23943
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
- Published
- 26 Aug 2026
Source:Apple Machine Learning ResearchT1observed 2 h agohigh
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Provenance
Attributed facts
6
Source tiers
T16
Freshest observation
2 h ago
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None
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- Published by
- Apple
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Claim history · Authors
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: Luce: Relightable Gaussians for 3D Asset Generation (Apple)
apple_ml
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| Apple Machine Learning Research | machinelearning.apple.com/rss.xml | feed | T1· Official | 2 h ago | 1 |
| Apple Machine Learning Research | machinelearning.apple.com/research/relightable-gaussians-3d-generation | paper_page | T1· Official | 2 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.