The Superb AI Physical AI Data Factory is a three-stage pipeline: capture real environments,
Why is robot training data so scarce?
Large language models are trained on trillions of tokens from the web, but the physical-world data robots need isn’t there.
Data that captures Korean apartment living or floor-sitting culture has never been published.
Without data, two options remain: deploy robots to learn by trial and error, or build the data they can learn from.
Superb AI turned the latter into a pipeline.
0
Public robot training datasets
from Korean home environments
80%+
Share of visual data in
all data generated worldwide
<1%
Share of visual data
converted into business value
How does the Data Factory run?
The Physical AI Data Factory is a three-stage pipeline: capture, assetize, generate. It collects human behavior in real environments,
converts it into simulator-ready digital assets, and recreates scenarios at scale as synthetic data.
STEP 01 · CAPTURE
Capture the real world
A timecode-synchronized, 17-camera multi-view rig captures human behavior in real homes and worksites, using 15 third-person cameras and two additional cameras for egocentric capture and 3D scanning.
MULTI-VIEW RIGRGB-DEGO-CENTRIC
STEP 02 · ASSETIZE
Convert to digital assets
3D Gaussian Splatting reconstructs the environment, SMPL extracts human motion, and SAM segments individual objects. Together, they turn captured data into digital assets that simulators can manipulate.
3D GAUSSIAN SPLATTINGSMPLSAM
STEP 03 · GENERATE
Generate synthetic data
NVIDIA Isaac Sim and domain randomization vary lighting, layouts, and camera angles to generate thousands of synthetic variations. The approach: create more scenarios, not film more homes.
NVIDIA ISAAC SIMDOMAIN RANDOMIZATION
Built and validated in the real world.
These results were achieved as part of the Korean government’s Sovereign AI Foundation Model project.
50
Real Korean homes
captured
50
Household task scenarios
reenacted 7,500 times
300K
400M raw frames refined into
high-fidelity assets (1.08M raw RGB-D frames)
17
Cameras in a timecode-synchronized
multi-view rig
We adapt the same end-to-end pipeline to your environment and robot.
With the full process already proven, we can quickly define timelines and quality targets.
And our work continues.
Phase 1 · Complete
Real-world dataset for Korean home environments
- 50 homes · 50 scenarios × 3 takes
- 17 viewpoints (15 third-person · 2 ego-centric)
- 1.08M raw RGB-D frames
- 300K frames assetized
Phase 2 · BuiltSynthesis · Ongoing
Simulation assets & synthetic data
- 50 spatial assets (3DGS)
- 5,000 motions · 10,000 object images
- 10,000 synthetic images
Why Superb AI
Superb AI is a vision intelligence company that turns visual data from production environments into intelligence enterprises can use.
A pipeline built on more than 130 million industrial images now feeds physical AI training data.
NVIDIA
NVIDIA's only physical AI partner in Korea
The only Korean company in the NVIDIA physical AI ecosystem, operating an Isaac Sim-based synthetic data pipeline.
Sovereign AI Foundation Model Project
Member of the LG AI Research Consortium
Responsible for real-environment data and simulation asset conversion in the Sovereign AI Foundation Model project.
Tech Stack
Proven pipeline. Integrated models.
Isaac Sim · 3D Gaussian Splatting · SMPL · SAM — connected to ZERO, Superb AI's industrial Vision Foundation Model.
What data does the factory build?
The data factory produces four types of data: spatial, motion, object, and synthetic —
combined to match wherever robots are deployed, from home service robots to manufacturing, logistics, and defense.
Spatial assets
Real spaces reconstructed as physics-ready 3D digital twins (3DGS)
Motion assets
Human motion and task sequences converted into robot-trainable motion data
Object assets
Real-world objects separated and refined into interactive simulation objects
Synthetic data
Domain randomization generates variations to expand training coverage
Dive deeper
Technical deep dives on each stage — an ongoing series.
Have questions?
Here are the ones we hear most.
What is the Physical AI Data Factory?
It is a system that produces training data for robots and physical AI systems through a structured pipeline. Superb AI’s Data Factory comprises three stages: real-world data capture, conversion into digital assets, and synthetic data generation. It has already converted 400 million frames collected from 50 real homes into training-ready assets.
Can synthetic data serve as reliable training data?
The quality of synthetic data is determined by the quality of its source assets. Superb AI generates synthetic data using digital twins reconstructed from real-world environments with 3D Gaussian Splatting, combining the accuracy of real-world measurements with the scalability of simulation.
Which industries does it apply to?
Any site with cameras and sensors qualifies. From residential data for home service robots to industrial data for manufacturing, logistics, and defense, Superb AI combines spatial, motion, object, and synthetic data to match your environment and robot form factor.
How is this different from data labeling?
Labeling adds information to data that already exists; the data factory creates the data itself. Built on a pipeline that has refined more than 130 million images, Superb AI operates the full process from capture through assetization and synthetic reproduction.
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How can we help
We’ll show you how Superb AI can help :
Curate effective datasets to improve model accuracy by >15%
Build more accurate computer vision datasets 10x faster
Train, diagnose, deploy AI models with just a few clicks
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"Superb Platform has been extremely useful and the AI-driven data curation makes it even more ideal. The ability to curate datasets and curate mislabels allows us to quickly and effortlessly build high-quality datasets."
Blaine Bateman, Chief Data Scientist
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