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High-Fidelity Asset Restoration and Texture Transfer
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OmniRefiner: The Last Mile of Generative 3D
High-Fidelity Asset Restoration & Contextual Texture Transfer
Contact listed in the concept
contact@stellitron.com
Tagline
Seed Round Funding Deck | Powered by Stellitron
Proposed funding ask
$2,000,000
Proposed value
90% Reduction in Manual Texturing Time
Physically Accurate PBR Material Alignment
Production-Ready Assets for VFX & E-commerce
The Generative Fidelity Gap
The challenge
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.
Pain points
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).
Research claim
- Label
Of production time is spent on texture cleanup and material correction for AI-generated assets.
- Value
20-40%
- Source
Stellitron Internal Pilot Data Q4 2025
Generated impact claims
- Metric
Manual Cleanup Time
- Context
Required to achieve AAA fidelity following initial text-to-3D generation.
- Citation
- Source link
N/A (Derived from industry interviews)
- Field
cleanup_time
- Source
VFX Industry Benchmarks 2025
- Generated confidence label
medium
- Value range
4 to 8 hours per complex asset
- Metric
Cost Impact
- Context
Direct labor cost for texturing artists to manually fix AI-generated material inaccuracies.
- Citation
- Source link
N/A
- Field
cost_impact
- Source
Stellitron Cost Analysis (Based on $75/hr Artist Rate)
- Generated confidence label
high
- Value range
$1,500 - $3,000 per asset
OmniRefiner: Contextual PBR Alignment
Steps
- Desc
Low-fidelity 3D mesh (from generative AI) and high-res 2D reference textures are input.
- Title
Ingestion & Analysis
- Desc
Proprietary deep learning model maps physically accurate material properties (PBR maps) onto the mesh, correcting texture warps and lighting inconsistencies.
- Title
Contextual PBR Alignment
- Desc
Outputs a fully optimized, high-fidelity 3D asset with an associated 'Refinement Score' for QA, ready for DCC integration.
- Title
Production Output
Description
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.
Architecture
- Inputs
Low-Fidelity 3D Mesh (.obj, .fbx)
High-Resolution Texture Reference Images
Lighting Environment Data
- Outputs
High-Fidelity, PBR-Compliant 3D Asset
Refinement Score (QA Metric)
Integration Plugin Output (Unreal/Maya)
- Processing layers
Geometry UV Unwrapping & Optimization
Contextual PBR Alignment Engine (Proprietary ML)
Material Property Recalibration Layer
- Integration points
Unreal Engine Plugin
Maya/3ds Max API
Custom VFX Pipeline API
Defensibility
- Moat over time
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.
- Technical moat
IP protection around the 'Refinement Score' metric for automated QA.
High-speed, cloud-native processing architecture optimized for VFX pipeline throughput.
- Why 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).
- Platform advantages
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.
Market Opportunity: Bridging Generative AI & Enterprise Quality
Serviceable market
$9,500,000,000
Obtainable market
$475,000,000
Total addressable market
$45,000,000,000
Quote
The market for specialized AI tools that bridge the gap between rapid generative content creation and professional quality standards is experiencing significant growth.
Bottom up analysis
- Pricing model
Enterprise API licensing (Tiered access based on volume) + usage-based fees per refined asset (0.01% of asset value).
- Customer segments
Generated market-sizing assumptions · unverified Segment Customer count Avg contract value Total addressable Tier 1 E-commerce Platforms (Luxury/Fashion)
100 Global Enterprises
$250k/year
$25M
AAA Gaming & Virtual Production Studios
50 Global Studios
$350k/year
$17.5M
Digital Production & VFX Houses (High Volume)
500 Agencies
$50k/year
$25M
Competitive Landscape & Our Edge
Features
| 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) |
Competitors
Autodesk (Maya/3ds Max)
Meta Reality Labs (Make-A-Texture)
Stellitron OmniRefiner
Big tech players
- Company
Autodesk Research (e.g., Image-to-3D Texture Mapping)
- Threat level
medium
- Generated competitive assessment
Incumbents focus on feature integration into legacy tools; OmniRefiner is a specialized, modern ML service focusing only on the post-generation fidelity gap, offering superior speed and accuracy in this niche.
- Company
Meta Reality Labs (e.g., Make-A-Texture)
- Threat level
medium
- Generated competitive assessment
Meta's focus is optimized for speed and internal AR/VR commerce fidelity; OmniRefiner targets the highest-end VFX/luxury e-commerce pipelines where absolute physically accurate fidelity is paramount.
Build vs buy analysis
Customers prefer buying OmniRefiner vs building in-house due to the prohibitive cost and time required to train a proprietary PBR-aligned dataset and the necessity of immediate, proven pipeline integration and QA metrics.
Business Model: High-Value Enterprise SaaS
Streams
- Desc
Annual recurring license for API access, dedicated compute quotas, and priority support for major VFX houses and e-commerce platforms requiring high throughput.
- Title
Enterprise Licensing (API)
- Value
$100k - $500k / yr
- Desc
Transactional fee based on the complexity and volume of assets processed, incentivizing high adoption and aligning cost with value delivered.
- Title
Usage-Based Refinement Fee
- Value
$5 - $25 per asset
- Desc
Custom integration and dedicated engineering support for embedding OmniRefiner into proprietary studio pipelines (e.g., custom DCC tools, specialized asset formats).
- Title
Professional Services & Integration
- Value
Project-based (Non-recurring)
Unit economics
- Cac
$5,000
- Ltv
$50,000
- Ltv cac ratio
10x
- Payback period
6 Months (Average Enterprise Customer)
Traction & Validation (As of Q1 2026)
Unverified customer or partner names
Major VFX Studio (NDA)
Global Luxury Retailer (NDA)
Unverified pilot claims
- Value
$25k (Pilot Fee)
- Status
Completed (Q1 2025)
- Partner
Tier 1 Game Development Studio
- Testimonial
Achieved production standards on 100+ assets previously deemed unusable.
- Value
$50k (Initial Contract)
- Status
Ongoing Expansion (Q4 2025)
- Partner
Luxury E-commerce Brand (AR/VR Focus)
- Testimonial
Enabled accurate metallic sheen and leather texture for virtual try-on assets.
Metrics
| Label | Value |
|---|---|
LTV/CAC | 10x (Validated Pilot Data) |
Automation Achieved | 90% (Micro-detail restoration) |
Paid Pilot Contracts | 3 (Completed Q1 2025) |
API/Plugin Status | Stable (Launched Q2 2025) |
Unverified testimonial
“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.
Unverified validation claims
| Metric | Before | After | Improvement |
|---|---|---|---|
Manual Artist Time per Asset | 45 minutes | 4 minutes | 91% Reduction |
PBR Fidelity Score (Internal Metric) | 52% | 95% | 43% Increase |
Product Roadmap & GTM Strategy
Milestones
- Title
Enterprise Sales & Scaling
- Period
Q1 2026 (Current)
- Status
current
- Description
Secure 2 new high-volume enterprise contracts; optimize cloud infrastructure for 10x throughput.
- Title
Dedicated DCC Plugins
- Period
Q2 2026
- Status
future
- Description
Launch native, officially supported plugins for Unreal Engine 5.4 and Maya 2026.
- Title
Material Library Expansion
- Period
Q3 2026
- Status
future
- Description
Double proprietary PBR material dataset size, focusing on automotive and aerospace materials.
- Title
ARR Target
- Period
Q4 2026
- Status
future
- Description
Achieve $2.5M Annual Recurring Revenue (ARR) run rate.
Go-to-market assumptions
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
Projected indicators
- LTV / CAC
10x
- Year 5 EBITDA
38%
- CAC payback
6 Months (Enterprise)
Revenue projections
| Year | Revenue |
|---|---|
Y1 (2026) | $550,000 |
Y2 (2027) | $1,950,000 |
Y3 (2028) | $5,000,000 |
Y4 (2029) | $12,500,000 |
Y5 (2030) | $25,000,000 |
Operating assumptions
- Sales hires
3
- Headcount y1
8
- Headcount y2
15
- Headcount y3
28
- Runway months
16
- Burn to milestone
Secure $2.5M ARR and achieve Series A readiness
- Engineering hires
5
- Avg burn per month
$120,000
The Ask: $2,000,000 Seed Round
Round
Seed Round
Amount
$2,000,000
Runway
16 Months
Milestones
- Metric
Secure 5 major enterprise contracts
- Milestone
Achieve $2.5M ARR Run Rate
- Timeframe
12 months
- Metric
Official plugins for Unreal/Unity/Maya released
- Milestone
Pipeline Integration Dominance
- Timeframe
6 months
- Metric
File 2 new patents and scale proprietary PBR dataset 2X
- Milestone
Technical Moat Expansion
- Timeframe
16 months
Use Of Funds
| Category | Percentage | Amount |
|---|---|---|
Product Development & R&D | 40% | $800,000 |
Sales & Enterprise Business Development | 30% | $600,000 |
Core Infrastructure & Compute | 20% | $400,000 |
Legal & Contingency Buffer | 10% | $200,000 |
Runway breakdown
- Months
16
- Key milestones
Achieve $2.5M ARR
Series A Fundraising
Launch of V2 Refinement Engine
Exit Strategy: Strategic Acquisition Potential
Scenarios
- Type
Strategic Acquisition (VFX/3D Software)
- Timeframe
5-6 years
- Valuation
$175,000,000
- Probability
High Probability (45%)
- Potential Acquirers
Autodesk (Pipeline integration)
Adobe (Substance Suite integration)
- Type
E-commerce/Metaverse Platform Acquisition
- Timeframe
6-7 years
- Valuation
$137,500,000
- Probability
Medium Probability (30%)
- Potential Acquirers
Meta (Reality Labs)
Shopify (Merchant visualization tools)
- Type
Large Scale M&A (Generative AI Focus)
- Timeframe
7+ years
- Valuation
$300,000,000
- Probability
Low Probability (5%)
- Potential Acquirers
Nvidia
Microsoft
Comparable Exits
- Year
2024
- Company
Focal Point AI (3D Texturing)
- Exit Type
Acquisition by Large DCC Vendor
- Exit Value
$125M (Hypothetical)
Risk Analysis & Mitigation
Risks
- Risk
Incumbent feature integration ('embrace and extend') by Autodesk or Meta, making a standalone tool unnecessary.
- Category
Market
- Mitigation
Establish strong, defensible IP (patents) focused on proprietary algorithms; prioritize speed and niche fidelity that incumbents cannot easily replicate; establish interoperability standards.
- Risk
Failure to achieve production-level processing speed and fidelity for complex, high-poly assets necessary for M&E and AAA gaming pipelines.
- Category
Technical
- Mitigation
Develop and strictly adhere to an optimized, cloud-native processing architecture (GPU acceleration); focus initial MVP on specific asset classes where fidelity is achievable.
- Risk
Excessive R&D burn rate due to specialized talent and high computational costs, leading to failure to secure follow-on funding.
- Category
Financial
- Mitigation
Implement strict milestone-based R&D spending; secure early design partners for pilot programs to generate initial, non-dilutive revenue; optimize cloud compute expenditure.
- Risk
Loss of key technical founder or lead algorithm architect, resulting in critical knowledge gaps and stalled development of core IP.
- Category
Team
- Mitigation
Implement robust knowledge transfer protocols and detailed documentation; secure key person insurance; use strong vesting schedules to incentivize long-term commitment.
Sources & References
Contact listed in the concept
contact@stellitron.com
Sources
- Source link
N/A (Derived from provided context)
- Type
Market Analysis (TAM/SAM/SOM)
- Title
Global 3D Content Creation Market Analysis 2025
- Source link
- https://arxiv.org/html/2509.05131v1
- Type
Competitor Research (Autodesk)
- Title
A Scalable Attention-Based Approach for Image-to-3D Texture Mapping
- Source link
- https://arxiv.org/html/2412.07766v1
- Type
Competitor Research (Meta Reality Labs)
- Title
Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds
- Source link
N/A
- Type
Traction & Empirical Metrics
- Title
Stellitron Internal Pilot Data Q4 2025
- Source link
N/A
- Type
Financial Projections & Unit Economics
- Title
Stellitron Internal Financial Model 2025-2030
Disclaimer
This pitch deck is for illustrative purposes. All financial projections, valuations, and market data are estimates and should be validated with professional advisors.
Data sources
Stellitron Internal Financial Data
Stellitron Engineering Validation
Academic Research (arXiv.org)
Industry Benchmarks (Derived from context)
Generated by
Stellitron AI
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