Synthetic Data Annotation Services
Bridge the sim-to-real gap with expert annotation of simulated training data from Australia's trusted synthetic data labeling specialists.
Why Synthetic Data Annotation Matters
Synthetic data is revolutionizing AI development—enabling unlimited training data generation, rare scenario creation, and privacy-preserving model training. But simulation alone isn't enough. Even photorealistic synthetic data requires expert human annotation to ensure accurate labels, validate realism, and create ground truth that transfers to real-world performance.
Trusted by autonomous vehicle companies, robotics developers, gaming AI teams, and digital twin platforms to annotate millions of synthetic images, validate simulation realism, and bridge the sim-to-real gap.
Simulated Environment Labeling
Comprehensive annotation for 3D simulated worlds and virtual environments
3D Scene Understanding
Annotate objects, surfaces, boundaries, and spatial relationships in fully simulated 3D environments. Label scene composition and object hierarchies.
Virtual World Mapping
Create semantic maps of simulated environments including navigable areas, obstacles, terrain types, and environmental zones.
Physics & Dynamics Annotation
Label object physics properties, material characteristics, collision behaviors, and dynamic interactions in physics-based simulations.
Lighting & Rendering Conditions
Annotate lighting scenarios, shadow patterns, reflections, time-of-day conditions, and weather effects in rendered scenes.
Multi-Modal Synthetic Data
Label synchronized synthetic RGB, depth, infrared, LiDAR, and radar data for multi-sensor fusion training.
Scenario Classification
Categorize synthetic scenarios by complexity, realism level, edge case characteristics, and training value.
CGI & Rendered Image Annotation
Photorealistic Rendering Annotation
Label objects, materials, textures, and scene elements in high-fidelity CGI renders for synthetic-to-real transfer.
3D Model Annotation
Annotate 3D assets, character models, props, and environmental objects with semantic labels and material properties.
Product Visualization Labeling
Label synthetic product renders with attributes, features, and contexts for e-commerce AI training.
Character & Avatar Annotation
Label human figures, poses, clothing, expressions, and actions in CGI characters for gaming and virtual human AI.
Material & Texture Classification
Classify surface materials (metal, wood, fabric, glass) and texture properties in rendered images.
Game Engine Data Labeling
Unity & Unreal Engine Annotation
Label game engine-generated training data with object classes, instance IDs, semantic segmentation, and bounding boxes.
Procedurally Generated Content
Annotate infinite variations of procedurally generated environments, objects, and scenarios for scalable training data.
Video Game Dataset Creation
Extract and label training data from video games including GTA V, Minecraft, and simulation games.
NPC & AI Behavior Labeling
Annotate non-player character behaviors, interactions, pathfinding, and AI agent actions in game simulations.
Interactive Environment Annotation
Label interactive objects, affordances, physics interactions, and environment responses in game scenarios.
Digital Twin Annotation
Industrial Digital Twin Labeling
Annotate factory floor layouts, equipment positions, material flows, and operational states in industrial simulations.
Smart City Digital Twin Annotation
Label urban infrastructure, traffic patterns, building usage, and city operations in city-scale environments.
Asset & Equipment Annotation
Classify machinery, tools, vehicles, and assets within digital twin environments for monitoring AI.
Process & Workflow Labeling
Annotate manufacturing processes, assembly sequences, logistics workflows, and operational procedures.
Predictive Maintenance Scenarios
Annotate equipment degradation, failure modes, and maintenance scenarios for predictive AI training.
Validation & Quality Assessment
Realism Scoring
Evaluate and label the photorealism, physical plausibility, and real-world transferability of synthetic data.
Domain Gap Analysis
Annotate differences between synthetic and real data including visual artifacts and unrealistic elements.
Artifact Detection
Identify rendering artifacts, simulation glitches, texture problems, and synthetic-specific anomalies.
Distribution Matching Validation
Verify that synthetic data distributions match real-world data across lighting, weather, and scenario diversity.
Simulation-Specific Annotation
Perfect Ground Truth Generation
Create pixel-perfect segmentation masks, exact 3D bounding boxes, and noise-free labels impossible to obtain from real data.
Occlusion & Depth Labeling
Annotate object occlusion relationships, depth ordering, and 3D spatial layering with simulation-perfect accuracy.
Material Property Annotation
Label physical material properties (reflectivity, roughness, transparency) that affect visual appearance.
Temporal Consistency Labeling
Ensure object tracking, motion trajectories, and temporal relationships remain consistent across synthetic sequences.
Synthetic Data Domain Expertise
AI Taggers employs simulation-trained annotators who understand 3D graphics and game engine workflows.
3D graphics fundamentals
Knowledge of rendering pipelines, 3D modeling, textures, shaders, lighting, and computer graphics principles.
Game engine workflows
Familiarity with Unity, Unreal Engine, and game development tools used for synthetic data generation.
Simulation technologies
Understanding of physics engines, procedural generation, domain randomization, and simulation techniques.
Sim-to-real transfer challenges
Recognition of domain gaps, synthetic artifacts, and factors affecting real-world model performance.
Computer vision concepts
Deep understanding of how synthetic data trains perception models and what ground truth formats are needed.
Synthetic Data Quality Standards
Multi-stage validation process
Every synthetic annotation passes through annotator → simulation specialist → realism validator → quality auditor checkpoints.
100% consistency verification
Real experts validate that synthetic labels maintain perfect consistency with simulation ground truth.
Realism assessment protocols
Systematic evaluation of synthetic data quality, photorealism, and real-world transferability.
Domain gap documentation
Identify and document differences between synthetic and real data for effective domain adaptation.
Perfect ground truth validation
Leverage simulation capabilities to create noise-free, pixel-perfect annotations with validated accuracy.
Scalability for Synthetic Data Projects
From prototype datasets to production-scale generation with automated pipeline integration.
Synthetic images annotated
Simulation platforms
Perfect ground truth
Pipeline integration
Synthetic Data AI Use Cases
Autonomous Vehicle Training
Generate unlimited driving scenarios including rare edge cases and dangerous situations impossible to capture safely.
Robotics Development
Train robotic manipulation, navigation, and perception systems in simulation with perfect ground truth.
Retail & E-commerce
Create infinite product variations and studio-quality images without physical photography.
Healthcare & Medical AI
Generate synthetic medical images for rare diseases and privacy-preserving training datasets.
Industrial Automation
Simulate manufacturing scenarios, defect conditions, and equipment configurations for quality control AI.
Privacy-Preserving AI
Train facial recognition and person detection systems without collecting sensitive personal data.
Data Augmentation
Supplement limited real-world datasets with synthetic samples covering gaps and rare scenarios.
Simulation-Based RL
Label synthetic environments and reward signals for training RL agents in simulated worlds.
Simulation Platforms We Support
Why Synthetic Data Teams Choose AI Taggers
Simulation expertise
Annotators trained in 3D graphics, game engines, and synthetic data generation understand unique characteristics.
Realism validation
Expert assessment of whether synthetic data will transfer effectively to real-world model performance.
Perfect ground truth
Leverage simulation capabilities for pixel-perfect, noise-free annotations impossible with real data.
Game engine integration
Native workflows for Unity, Unreal, and major simulation platforms with direct export support.
Scale on demand
Annotation capacity matching your procedural generation pipelines for unlimited data volume.
Synthetic Data Annotation Process
Synthetic Data Assessment
We review your simulation pipeline, rendering quality, ground truth requirements, and sim-to-real goals. Our experts develop annotation specifications.
Pilot Batch Validation
Annotate 500-1,000 synthetic samples with realism assessment. You evaluate quality and domain gap analysis. We calibrate workflows.
Production with Realism QA
Distributed synthetic data teams process your generated imagery with continuous realism and consistency monitoring.
Delivery with Transfer Validation
Receive annotations with perfect ground truth, realism scores, and domain gap documentation for effective sim-to-real training.
Real Results From Synthetic Data Teams
"AI Taggers understood our sim-to-real challenges better than any other annotation provider—their realism validation caught domain gap issues before we wasted training cycles."
ML Engineering Lead
Autonomous Robotics Startup
"The perfect ground truth quality from their synthetic annotation was invaluable for validating our perception models—accuracy we couldn't achieve with real-world data."
Director of AI
Simulation Platform Company
Get Started With Expert Synthetic Data Annotation
Whether you're training autonomous systems, developing robotics AI, or creating privacy-preserving datasets, AI Taggers delivers the synthetic data annotation quality your sim-to-real pipeline needs.