Five Use Cases.
One Ground Truth Layer.
Voxelmaps digitizes reality and makes it accessible for Physical AI. Explore five real-world data foundations powering robotics, autonomous systems, and spatial intelligence.
High-Quality Gamer Movement for Physical AI.
A highly engaged VR gamer community contributes structured motion-capture sessions at scale — delivering precise, repeatable, and diverse human movement data for robotics, digital humans, and simulation platforms.
Recruit & Schedule
Efficient recruitment of VR-native participants from engaged gaming communities, with streamlined scheduling for reliable session attendance.
Capture & Deliver
Structured motion-capture sessions in immersive, controlled environments produce consistent, high-quality datasets with exceptional session reliability.
What We Capture
Locomotion, balance, manipulation, gestures, and coordinated group activity across varied terrain and multi-person interaction dynamics.
Beyond Entertainment
Powers humanoid robotics, autonomous systems, imitation learning, healthcare rehab, defense training, and spatial computing.
Applications
Real-World Body Positioning at Scale.
An end-to-end platform for collecting high-fidelity human motion and body-positioning data — from participant recruitment through synchronized multi-camera capture to validated, deployment-ready datasets.
Participant Outreach
Scalable recruitment across diverse demographics, body types, and occupations — capturing the variation AI will encounter in deployment.
Multi-Camera Arrays
Synchronized, high-resolution camera systems capture every angle simultaneously for full skeletal articulation.
What Gets Captured
Joint angles, gait, posture transitions, object interactions, and environmental context — far beyond scripted lab actions.
Validation Pipeline
Rigorous quality checks ensure accuracy, completeness, and consistency before datasets reach your training pipeline.
Applications
Authentic Human Workflows, Captured at Work.
Wearable cameras and sensors record fully natural, unscripted human behavior during everyday work. A first-person view of real operational reality — unlike anything synthetic simulations can produce.
Why It Matters
Lab simulations miss real-world variability, edge cases, and the organic decision-making that defines real operational environments.
Where It's Captured
Manufacturing, logistics, healthcare, construction, retail, and corporate operations — the full spectrum of real human work.
A Unique View
First-person perspective gives AI developers insight into intent, task sequencing, situational awareness, and operational context.
From Capture to Deploy
Wearables record, raw footage is structured into labeled datasets, models train on authentic workflows, and systems deploy with deeper understanding.
Applications
Verified Identity for the Next Generation of AI.
A curated 3D facial data ecosystem combining liveness detection, document verification, biometric deduplication, and quality assurance — achieving ~98% eligibility accuracy for mission-critical AI.
Why Legacy Data Fails
Inconsistent quality, duplicate identities, demographic gaps, and unverified entries corrupt traditional facial datasets.
Four-Step Verification
Liveness, document verification, biometric deduplication, and human-reviewed quality assurance before any contribution is accepted.
What Makes 3D Different
Full facial geometry and depth, dynamic expression capture, and biometric characteristics — far beyond flat 2D photographs.
Diversity at Scale
Multiple age groups, ethnicities, environments, lighting, expressions, and capture angles for equitable model performance.
Applications
Replace Synthetic Data with Lived-In Reality.
Participants voluntarily contribute anonymized spatial data from real homes and workplaces. Privacy-preserving digital twins of lived-in environments train AI on the world as it actually is.
Why Synthetic Falls Short
Simulated models miss real furniture, authentic movement, genuine lighting, accessibility challenges, and true human-space interactions.
Privacy-Preserving
All spatial data is anonymized and contributed voluntarily — user control and trust at every step of the pipeline.
Continuously Evolving
The dataset grows over time, capturing changing behaviors, layouts, and spatial patterns as real environments evolve.
Measurable Lift
Models trained on lived-in environments show 3× adaptability in unstructured real-world spaces compared to synthetic-only training.
Applications
Build with real-world data.
From motion to spaces to identity — bring the ground truth your AI needs to perform in the world as it truly is.