Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging
Hi3DGen generates high-fidelity 3D geometry from images using normal bridging, achieving superior detail fidelity.
Key Findings
Methodology
Hi3DGen comprises three core components: an image-to-normal estimator (NiRNE) using noise injection and dual-stream training for sharp and stable normal estimation; a normal-to-geometry learning module (NoRLD) leveraging normal-regularized latent diffusion learning; and the DetailVerse dataset, a high-quality synthetic 3D asset repository supporting training.
Key Results
- NiRNE achieves a normal angle error (NE) of 21.837 on LUCES-MV, outperforming existing methods by ~22%.
- Hi3DGen generates 3D models with superior detail fidelity compared to SOTA methods like Trellis and Clay, especially in complex geometric scenarios.
- The DetailVerse dataset significantly improves performance, boosting NE and SNE by 0.4 and 1.7, respectively.
Significance
This research addresses limitations in reproducing fine-grained geometric details, offering a new direction for image-to-3D generation. It has significant implications for industries such as gaming, VR, and digital content creation.
Technical Contribution
Hi3DGen introduces normal maps as intermediate representations, combining noise-injected regression and normal-regularized diffusion learning, alongside the DetailVerse dataset to enhance detail fidelity and stability.
Novelty
Hi3DGen is the first framework to use normal maps for bridging image-to-3D generation, innovating with noise-injected regression and normal-regularized diffusion learning to address detail loss and domain gaps.
Limitations
- The normal estimator struggles under extreme lighting conditions, leading to detail loss.
- The DetailVerse dataset relies on complex prompt engineering, which may limit scalability.
- Generated models require further validation in real-world scenarios.
Future Work
Future work could explore extending the normal estimator to multi-view scenarios, optimizing data synthesis pipelines, and investigating the applicability of normal regularization to other 3D generation tasks.
AI Executive Summary
With the increasing demand for high-fidelity 3D models from images, existing methods face challenges in reproducing fine-grained geometric details due to domain gaps and ambiguities in RGB images. Hi3DGen addresses these issues by leveraging normal maps as intermediate representations, significantly enhancing detail fidelity.
The framework consists of three key components: an image-to-normal estimator (NiRNE) using noise injection and dual-stream training for sharp and stable normal estimation; a normal-to-geometry learning module (NoRLD) employing normal-regularized latent diffusion learning; and the DetailVerse dataset, a high-quality synthetic 3D asset repository supporting training.
Experiments demonstrate Hi3DGen's superiority in normal estimation and geometry generation, outperforming state-of-the-art methods in detail fidelity, particularly in complex scenarios. This research offers new directions for image-to-3D generation and holds significant potential for applications in gaming, VR, and digital content creation.
Deep Analysis
Background
Recent years have seen growing interest in image-to-3D geometry generation, but existing methods struggle with reproducing fine-grained geometric details. Traditional approaches rely on direct RGB-to-3D mapping, limited by domain gaps and RGB ambiguities.
Core Problem
Existing methods fail to generate complex geometric details, especially in real-world scenarios. Key bottlenecks include domain gaps and RGB image ambiguities.
Innovation
Hi3DGen bridges image-to-3D generation using normal maps, introducing noise-injected regression and normal-regularized diffusion learning, combined with the DetailVerse dataset to enhance detail fidelity.
Methodology
- �� Image-to-normal estimator: Noise injection enhances high-frequency feature learning; dual-stream architecture separates low- and high-frequency information.
- �� Normal-to-geometry learning: Normal-regularized latent diffusion learning provides fine-grained supervision.
- �� DetailVerse dataset: Text-to-image-to-3D synthesis pipeline creates high-quality synthetic data.
Experiments
Experiments use LUCES-MV for normal estimation validation and DetailVerse for geometry generation training. Baselines include Trellis, Clay, and others.
Results
NiRNE achieves a normal angle error of 21.837 on LUCES-MV, outperforming existing methods by ~22%. Hi3DGen generates 3D models with superior detail fidelity compared to SOTA methods.
Applications
Hi3DGen can be applied in gaming, VR, and digital content creation, particularly in scenarios requiring high-fidelity geometric details.
Limitations & Outlook
The normal estimator struggles under extreme lighting conditions, the DetailVerse dataset generation process is complex, and real-world validation of generated models is needed.
Plain Language Accessible to non-experts
Think of Hi3DGen as a factory. First, an image enters the 'normal workshop,' where noise injection and dual-stream processing create sharp normal maps. These maps then move to the 'geometry workshop,' where normal regularization guides the creation of high-fidelity 3D models. The process relies on a high-quality 'raw material library' (DetailVerse dataset) to ensure the final product's quality.
ELI14 Explained like you're 14
Imagine you're playing a game and want to turn a photo into a super-realistic 3D character. Hi3DGen is like a magic tool that first turns the photo into a 'normal map,' like the character's skeleton and skin texture. Then, it uses this map to create a super-detailed 3D model — even the hair strands are visible! Cool, right?
Glossary
Normal Map
An image encoding surface orientation, providing geometric detail cues.
Used to bridge image-to-3D generation.
Noise Injection
A technique to enhance sensitivity to high-frequency features by adding noise.
Used in the normal estimator.
Latent Diffusion Learning
A method for diffusion training in compressed latent spaces.
Used for normal-to-geometry generation.
DetailVerse Dataset
A synthetic dataset of 700k high-quality 3D assets.
Supports normal estimation and geometry generation training.
Dual-Stream Architecture
A network design separating low- and high-frequency features.
Used in the normal estimator.
Open Questions Unanswered questions from this research
- 1 How can the normal estimator perform better under extreme lighting conditions?
- 2 How can the DetailVerse dataset synthesis pipeline be optimized for scalability?
Applications
Immediate Applications
Game Design
Generate high-fidelity 3D characters and scenes to enhance visual effects.
Virtual Reality
Create realistic 3D environments for immersive experiences.
Long-term Vision
Digital Twins
Generate high-fidelity 3D models of real-world objects for industrial and urban planning.
Abstract
With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due to limitations in domain gaps and inherent ambiguities in RGB images. To address these issues, we propose Hi3DGen, a novel framework for generating high-fidelity 3D geometry from images via normal bridging. Hi3DGen consists of three key components: (1) an image-to-normal estimator that decouples the low-high frequency image pattern with noise injection and dual-stream training to achieve generalizable, stable, and sharp estimation; (2) a normal-to-geometry learning approach that uses normal-regularized latent diffusion learning to enhance 3D geometry generation fidelity; and (3) a 3D data synthesis pipeline that constructs a high-quality dataset to support training. Extensive experiments demonstrate the effectiveness and superiority of our framework in generating rich geometric details, outperforming state-of-the-art methods in terms of fidelity. Our work provides a new direction for high-fidelity 3D geometry generation from images by leveraging normal maps as an intermediate representation.