Capture once.
Train every condition.
Syntheta converts real factory, robotic, drone, and egocentric captures into labeled, validated training data. Generate controlled variation with Real2Robust, FunctaGen, and a clean passthrough baseline, while preserving source provenance and quality checks.
Capture is the bottleneck.
Real-world capture is slow and rarely covers every operating condition. Syntheta takes the data you already have, restores and transforms it, then generates labeled variants for configurable industrial targets such as pallets, tools, robot arms, parts, defects, workers, and safety zones. NeuroDepth-T4 handles restoration, Real2Robust applies bounded domain variation, and FunctaGen provides learned synthesis.
One capture.
A labeled dataset.
A source capture flows through restoration, target labeling, controlled generation, validation, dataset export, and model training. Each accepted sample keeps its source, recipe, seed, labels, and validation record.
Label once
A configurable target adapter labels the classes selected for the run, including industrial objects, people, robot components, parts, and safety regions.
NeuroDepth-T4 restoration
EventCore V8.1 restores low-light frames using per-pixel brightness-change processing, with device, quality, and fallback state recorded in the manifest.
Branch A — R2R mutation
Real2Robust (R2R) applies domain-gap mutations — simulated weather, lighting shifts, sensor noise — to the restored frames. Labels carry forward from stage 1, no re-detection needed.
Branch C — passthrough
The clean, un-mutated baseline — restored frames copied through with the same carried-forward labels.
FunctaGen — neural in-between frames
An implicit neural representation learns a compact scene-conditioned representation and produces source-anchored synthetic frames when its quality gates pass.
Branch B — fresh detection + R2R
Generated frames are checked with target-aware detection and validation before they enter the training dataset. Fallback provenance is recorded instead of silently treating rejected output as ground truth.
Summary
Every branch's output manifest rolls up into one summary file for the whole run.
3D capture + human compositing — in progress
SfM reconstruction is real and working — 1,110 points reconstructed from 35 camera poses on a test run. Gaussian-splat training and SMPL-X human compositing into the rebuilt scene are next, currently blocked on GPU/CUDA and licensed SMPL-X/AMASS assets in this build environment, so it stops honestly rather than faking a render.
From one real capture
Inspect the transformation chain: real capture, restoration, mutation, generated labels, and 3D reconstruction.
Numbers, not adjectives
What's actually running
Get early access.
Bring a real capture and your target classes. We return an expanded dataset, labels, provenance, and a training baseline.
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