Luce: Relightable Gaussians for 3D Asset Generation
Luce uses Gaussian clouds for single-image to 3D asset generation, improving FID by 28%.
Key Findings
Methodology
Luce employs a voxelized multimodal Gaussian cloud to unify geometry and PBR materials, 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 latent from a single image, conditioned on multi-layer features from a pretrained image encoder that preserve semantic context and spatial detail. The latent decodes into relightable PBR Gaussians and an optional textured mesh.
Key Results
- On the Toys4K dataset, Luce achieves state-of-the-art single-image-to-3D generation, improving FID by 28% over the strongest baseline.
- On the AI-generated image benchmark, Luce improves the CLIP image-alignment score from 0.8299 to 0.8519.
- Luce generates assets with high geometric accuracy and material fidelity, preserving fine details such as text and logos.
Significance
Luce holds significant implications for both academia and industry. It addresses the challenge of unified representation of geometry and appearance in high-fidelity image-to-3D generation, supporting relighting and integration into standard rendering pipelines. By improving FID and CLIP scores, Luce opens new possibilities for high-quality 3D asset generation.
Technical Contribution
Luce's technical contributions include its innovative multimodal Gaussian cloud representation, combined with a variational autoencoder and rectified-flow transformer, offering distinct theoretical guarantees and engineering possibilities compared to existing methods. It achieves significant performance improvements in single-image-to-3D generation.
Novelty
Luce is the first to integrate geometry and PBR materials into a multimodal Gaussian cloud, using a variational autoencoder to compress the representation. This innovation lies in its ability to generate and decode a material-aware latent space, offering significant advantages over existing methods.
Limitations
- Luce may struggle with complex textures and highly reflective surfaces, as these features challenge the representation capacity of Gaussian clouds.
- The method relies on a pretrained image encoder, which may limit its generalization to specific domains.
Future Work
Future research could explore Luce's performance on broader datasets and improve its handling of complex materials and dynamic scenes. Additionally, further optimization of computational efficiency and reducing reliance on pretrained models are important directions.
AI Executive Summary
Luce is an innovative 3D representation method designed to address the challenge of unified representation of geometry and appearance in high-fidelity image-to-3D generation. Existing methods often fall short in integrating geometry and appearance, making it difficult to support relighting and standard rendering pipeline integration.
Luce unifies geometry and PBR materials in a voxelized multimodal Gaussian cloud, using a variational autoencoder and rectified-flow transformer to generate and decode a material-aware latent space. The method achieves state-of-the-art single-image-to-3D generation on the Toys4K dataset, improving FID by 28%.
Luce generates 3D assets with high geometric accuracy and material fidelity, preserving fine details such as text and logos. Despite challenges in handling complex textures and highly reflective surfaces, Luce offers new possibilities for high-quality 3D asset generation and holds significant implications for academia and industry.
Deep Analysis
Background
3D asset generation plays a crucial role in computer graphics and virtual reality applications. Traditional methods often rely on manual modeling or multi-view reconstruction, which are inefficient and lack high fidelity. Recently, deep learning-based approaches have emerged, significantly improving the efficiency and quality of 3D model generation from single images.
Core Problem
High-fidelity image-to-3D generation requires a unified representation of geometry and appearance. Existing methods struggle to support relighting and integration into standard rendering pipelines, making it difficult to generate geometrically accurate and materially faithful 3D assets.
Innovation
Luce's core innovation lies in its multimodal Gaussian cloud representation, combined with a variational autoencoder and rectified-flow transformer, to achieve a unified representation of geometry and PBR materials. By generating a material-aware latent space, Luce can decode into relightable PBR Gaussians and textured meshes.
Methodology
- �� Use voxelized multimodal Gaussian cloud to represent geometry and PBR materials
- �� Variational autoencoder compresses representation into material-aware latent space
- �� Rectified-flow transformer generates latent from a single image
- �� Latent decodes into relightable PBR Gaussians and textured mesh
Experiments
Experiments were conducted on the Toys4K dataset, comparing Luce with existing baseline methods. FID and CLIP image-alignment scores were used as evaluation metrics. Results demonstrate significant performance improvements in single-image-to-3D generation with Luce.
Results
Luce achieves state-of-the-art single-image-to-3D generation on the Toys4K dataset, improving FID by 28% over the strongest baseline. On the AI-generated image benchmark, Luce improves the CLIP image-alignment score from 0.8299 to 0.8519.
Applications
Luce can be applied in virtual reality, game development, and film production for 3D asset generation. Its high fidelity and relightable features make it suitable for scenarios requiring high-quality visual effects.
Limitations & Outlook
Luce may struggle with complex textures and highly reflective surfaces. Additionally, the method relies on a pretrained image encoder, which may limit its generalization to specific domains. Future research could explore improvements in these areas.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. Luce is like a smart chef who can infer a complete recipe from a photo of ingredients. This chef not only tells you the types of ingredients but also their textures and colors, just like Luce captures geometry and appearance information. Then, it uses this information to create a dish, similar to how Luce generates a 3D model. Even under different lighting, the dish remains delicious, just like Luce's 3D assets remain realistic under various lighting conditions.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game and see a super cool character. Luce is like a wizard that can turn a picture of that character into a 3D model! This model not only looks realistic but also stays cool under different lights. Just like when you play games in different places, the character always looks awesome! That's the magic of Luce, turning pictures into lifelike 3D. Isn't that amazing?
Glossary
Gaussian Cloud
A multimodal representation method for geometry and material information, leveraging Gaussian distribution properties.
Used in Luce to unify geometry and PBR materials.
Variational Autoencoder (VAE)
A generative model that learns the distribution of latent space to generate data.
Used to compress Luce's multimodal representation.
Rectified-flow Transformer
A model used to generate latent variables, combining characteristics of flow transformers.
Used to generate Luce's latent variables from a single image.
FID (Fréchet Inception Distance)
A metric for evaluating the quality of generated images; lower values indicate higher quality.
Used to evaluate Luce's performance on the Toys4K dataset.
CLIP Image-alignment Score
A metric for evaluating the consistency of generated images with textual descriptions.
Used to evaluate Luce's performance on the AI-generated image benchmark.
Open Questions Unanswered questions from this research
- 1 How to improve Luce's performance on complex textures and highly reflective surfaces remains an open question. Current methods are limited in these scenarios, requiring new technological breakthroughs.
Applications
Immediate Applications
Virtual Reality
Luce can be used to generate high-fidelity 3D assets in virtual reality, enhancing user experience. Requires high-quality input images and computational resources.
Long-term Vision
Film Production
Luce can be used for 3D asset generation in film production, reducing manual modeling time. Further optimization of computational efficiency is needed.
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 rectified-flow transformer generates this latent from a single image, conditioned on multi-layer features from a pretrained image 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.