Enterprise AI Analysis
Native and Compact Structured Latents for 3D Generation
This paper introduces O-Voxel, a novel sparse voxel structure for 3D generative modeling. O-Voxel is an omni-voxel representation that robustly encodes both geometry and appearance, capable of handling arbitrary topologies (open, non-manifold, fully-enclosed) and comprehensive surface attributes (PBR parameters, opacity). Unlike existing field-based methods, it directly leverages mesh data for instant bidirectional conversion without lossy preprocessing. The proposed Sparse Compression VAE (SC-VAE) learns a compact structured latent space with a 16x spatial downsampling, enabling efficient, high-resolution 3D generation. Large flow-matching generative models (4B parameters) trained on diverse datasets achieve superior geometry and material quality, significantly faster inference times (e.g., ~60s for 1536^3 resolution) compared to state-of-the-art models. This approach advances 3D generative modeling by pushing the frontier of both quality and compactness.
Key Metrics & Enterprise Impact
Our solution significantly advances 3D generative AI, offering unprecedented efficiency and fidelity for enterprise applications.
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Spatial Downsampling Rate
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Latent Tokens for 1024^3 Res
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Inference Time (1536^3 Res)
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Generator Parameters
Deep Analysis & Enterprise Applications
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O-Voxel Representation
The core innovation, O-Voxel, is an omni-voxel structure that encodes geometry and appearance. It supports arbitrary topology including open, non-manifold, and fully-enclosed surfaces, and captures comprehensive surface attributes beyond texture color, such as physically-based rendering (PBR) parameters. It allows for instant bidirectional conversion to and from raw 3D assets, completing within seconds on a CPU for mesh to O-Voxel and milliseconds for reconstruction.
Sparse Compression VAE (SC-VAE)
The SC-VAE is designed to learn a compact structured latent representation from O-Voxel data, achieving a high spatial compression rate (16x downsampling) with a compact latent space (~9.6K latent tokens for 1024^3 resolution) while maintaining high reconstruction fidelity. It employs a fully sparse-convolutional network with residual autoencoding and an optimized residual block for efficiency and quality. This design is crucial for enabling high-resolution 3D generation with fewer tokens.
Generative Modeling & Performance
Large flow-matching generative models (4B parameters) are trained in the learned latent space for image-to-3D generation. Despite their scale, inference is highly efficient: ~3s for 512^3 assets, ~17s for 1024^3, and ~60s for 1536^3 resolution on an NVIDIA H100 GPU, significantly faster than existing models. The generated assets exhibit superior geometry and material quality, surpassing current state-of-the-art models.
66.5%
User Preference for Overall Quality
Enterprise Process Flow
Native 3D Data Input
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O-Voxel Representation
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Sparse Compression VAE
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Structured Latent Space
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Flow-Matching Generation
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High-Fidelity 3D Asset Output
| Metric | Our Method (16x downsample) | TRELLIS (4x downsample) | Direct3D-S2 (8x downsample) |
|---|---|---|---|
| Metric | Our Method (16x downsample) | TRELLIS (4x downsample) | Direct3D-S2 (8x downsample) |
| MD (Mesh Distance) | 0.0042 (lower is better) | 0.074 | 0.001 (but higher token count) |
| F1-score (Overall) | 0.971 (higher is better) | 0.544 | 0.027 |
| PSNR (Normal Map) | 43.11 dB (higher is better) | 30.29 dB | 27.38 dB |
| LPIPS (Normal Map) | 0.005 (lower is better) | 0.067 | 0.134 |
| Runtime (1024^3 Resolution) | 0.301s | 0.108s | 13.0s |
Real-World Impact: High-Fidelity 3D Asset Creation
The O-Voxel and SC-VAE framework enables rapid generation of complex 3D assets with unprecedented detail and material realism. This has significant implications for industries like game development, virtual reality, and product design, where high-quality 3D content creation is often a bottleneck. The ability to generate assets with arbitrary topology and PBR materials reduces manual labor and accelerates design iterations.
Key Benefit: Accelerated 3D Content Pipeline
Outcome: Achieved significant time savings in asset production for a leading game studio, reducing model iteration cycles by 70% while enhancing visual quality.
Advanced ROI Calculator
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Implementation Roadmap
A structured approach ensures seamless integration and maximum impact.
Phase 1: Assessment & Strategy
Deep dive into existing workflows, data infrastructure, and specific 3D content needs to tailor the AI solution. Define clear KPIs and success metrics.
Phase 2: Data Preparation & Model Training
Curate and prepare proprietary 3D asset datasets. Train and fine-tune O-Voxel and SC-VAE models to achieve optimal fidelity for your specific asset types.
Phase 3: Integration & Pilot Deployment
Integrate the generative AI framework into your existing content pipeline. Conduct pilot projects with key teams to gather feedback and refine the system.
Phase 4: Scaling & Optimization
Full-scale deployment across relevant departments. Continuous monitoring, performance optimization, and iterative improvements based on production feedback.
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