OmniRefiner: The Last Mile of Generative 3D
High-Fidelity Asset Restoration & Contextual Texture Transfer
High-Fidelity Asset Restoration & Contextual Texture Transfer
The Generative Fidelity Gap
Current generative AI models efficiently produce macro geometry but fail to deliver the micro-level fidelity (accurate PBR material properties, specific texture granularity) required for professional production pipelines, negating the speed benefits of AI.
- ⚠High frequency details (fabric weave, metal brush) are lost or inaccurate in initial AI generation.
- ⚠VFX/E-commerce studios dedicate immense budget to manual material cleanup and correction.
- ⚠Inconsistent PBR materials cause visual artifacts when assets are rendered under different lighting conditions (a critical failure point for AR/VR commerce).
OmniRefiner: Contextual PBR Alignment
OmniRefiner is a specialized deep learning model that ingests low-fidelity AI assets and reference images, intelligently restoring fine physical details and ensuring materials are perfectly aligned with geometry and lighting, delivering production-ready assets automatically.
Ingestion & Analysis
Low-fidelity 3D mesh (from generative AI) and high-res 2D reference textures are input.
Contextual PBR Alignment
Proprietary deep learning model maps physically accurate material properties (PBR maps) onto the mesh, correcting texture warps and lighting inconsistencies.
Production Output
Outputs a fully optimized, high-fidelity 3D asset with an associated 'Refinement Score' for QA, ready for DCC integration.
System Architecture
- Low-Fidelity 3D Mesh (.obj, .fbx)
- High-Resolution Texture Reference Images
- Lighting Environment Data
- Geometry UV Unwrapping & Optimization
- Contextual PBR Alignment Engine (Proprietary ML)
- Material Property Recalibration Layer
- High-Fidelity, PBR-Compliant 3D Asset
- Refinement Score (QA Metric)
- Integration Plugin Output (Unreal/Maya)
- Unreal Engine Plugin
- Maya/3ds Max API
- Custom VFX Pipeline API
Why This Is Hard to Copy
- ✓Proprietary training dataset of millions of PBR materials mapped across diverse geometries.
- ✓Specialized deep learning architecture optimized for micro-detail restoration (Contextual PBR Alignment Engine).
- IP protection around the 'Refinement Score' metric for automated QA.
- High-speed, cloud-native processing architecture optimized for VFX pipeline throughput.
- OmniRefiner is an essential refinement layer, not a replacement for generative tools (like Autodesk or Meta).
- Deep integration into existing DCC platforms creates high switching costs.
- Dataset compounding advantage: Every new client asset processed contributes to model accuracy.
- Customer switching costs increase after proprietary API integrations are embedded in studio workflows.
- Refinement Score metric becomes the industry standard for automated material fidelity.
Market Opportunity: Bridging Generative AI & Enterprise Quality
“The market for specialized AI tools that bridge the gap between rapid generative content creation and professional quality standards is experiencing significant growth.”
Competitive Landscape & Our Edge
Competitive Landscape
| Feature | Autodesk (Maya/3ds Max) | Meta Reality Labs (Make-A-Texture) | Stellitron OmniRefiner |
|---|---|---|---|
| Micro-Detail Restoration & Fidelity | Low | Medium | High (Proprietary) |
| Contextual PBR Alignment | Low (General Mapping) | Medium (Shape-Aware) | High (Specialized ML) |
| VFX/DCC Pipeline Integration | High (Native) | Low (Internal Focus) | High (API/Plugin Focus) |
| Speed & Automation (Cleanup) | Medium (Manual Steps) | High (Fast Generation) | High (Automated Refinement) |
Business Model: High-Value Enterprise SaaS
Enterprise Licensing (API)
Annual recurring license for API access, dedicated compute quotas, and priority support for major VFX houses and e-commerce platforms requiring high throughput.
Usage-Based Refinement Fee
Transactional fee based on the complexity and volume of assets processed, incentivizing high adoption and aligning cost with value delivered.
Professional Services & Integration
Custom integration and dedicated engineering support for embedding OmniRefiner into proprietary studio pipelines (e.g., custom DCC tools, specialized asset formats).
Traction & Validation (As of Q1 2026)
““OmniRefiner is the only solution that bridges the gap between fast AI generation and film-ready quality, saving us hundreds of thousands in labor costs.” - Head of Digital Production, Major VFX Studio.”
Product Roadmap & GTM Strategy
Enterprise Sales & Scaling
Q1 2026 (Current)Secure 2 new high-volume enterprise contracts; optimize cloud infrastructure for 10x throughput.
Dedicated DCC Plugins
Q2 2026Launch native, officially supported plugins for Unreal Engine 5.4 and Maya 2026.
Material Library Expansion
Q3 2026Double proprietary PBR material dataset size, focusing on automotive and aerospace materials.
ARR Target
Q4 2026Achieve $2.5M Annual Recurring Revenue (ARR) run rate.
Market Entry Strategy
- ▹Targeted direct sales to the top 100 global luxury e-commerce and AAA gaming studios.
- ▹Partnerships with major 3D software vendors (e.g., Epic, Autodesk) for co-marketing and integration.
- ▹Content marketing focused on VFX efficiency and PBR fidelity standards.
Key Objectives
- ▹Achieve $2.5M ARR by Q4 2026.
- ▹Expand sales team to 4 dedicated Enterprise Account Executives.
- ▹File 2 additional patents related to Contextual PBR Alignment.
Financial Projections
Yearly Revenue Projections
Operating Assumptions & Burn Logic
Key Performance Indicators
The Ask: $2,000,000 Seed Round
Exit Strategy: Strategic Acquisition Potential
Exit Scenarios
Comparable Exits
Risk Analysis & Mitigation
Risk Analysis & Mitigation
Incumbent feature integration ('embrace and extend') by Autodesk or Meta, making a standalone tool unnecessary.
Establish strong, defensible IP (patents) focused on proprietary algorithms; prioritize speed and niche fidelity that incumbents cannot easily replicate; establish interoperability standards.
Failure to achieve production-level processing speed and fidelity for complex, high-poly assets necessary for M&E and AAA gaming pipelines.
Develop and strictly adhere to an optimized, cloud-native processing architecture (GPU acceleration); focus initial MVP on specific asset classes where fidelity is achievable.
Excessive R&D burn rate due to specialized talent and high computational costs, leading to failure to secure follow-on funding.
Implement strict milestone-based R&D spending; secure early design partners for pilot programs to generate initial, non-dilutive revenue; optimize cloud compute expenditure.
Loss of key technical founder or lead algorithm architect, resulting in critical knowledge gaps and stalled development of core IP.
Implement robust knowledge transfer protocols and detailed documentation; secure key person insurance; use strong vesting schedules to incentivize long-term commitment.
Sources & References
Generated by
Stellitron AI
Data Sources
Stellitron Internal Financial Data
Stellitron Engineering Validation
Academic Research (arXiv.org)
Industry Benchmarks (Derived from context)
References
Global 3D Content Creation Market Analysis 2025
Market Analysis (TAM/SAM/SOM)
A Scalable Attention-Based Approach for Image-to-3D Texture Mapping
Competitor Research (Autodesk)
Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
Competitor Research (Meta Reality Labs)
Stellitron Internal Pilot Data Q4 2025
Traction & Empirical Metrics
Stellitron Internal Financial Model 2025-2030
Financial Projections & Unit Economics
For inquiries, contact:
contact@stellitron.comThis pitch deck is for illustrative purposes. All financial projections, valuations, and market data are estimates and should be validated with professional advisors.