Is synthetic data a substitute for real capture?

For coverage of rare conditions, yes. For contact dynamics, deformable materials and lighting realism, not yet. The practical answer is a blend, with the real corpus anchoring the distribution and synthetic data extending its tails.

How do you prevent generated data from degrading a model?

Every generated sequence is replayed in a physics engine and rejected if contact forces or dynamics fall outside plausible bounds. Roughly 30 to 50 percent of generated sequences fail this gate, which is the point of running it.

Applied Intelligence

Training Data for World Models and Generative Video

Producing the real-world data that cannot otherwise be collected.

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Core Capabilities

World-Model-Driven Scene Generation

Generate plausible variations of captured scenes to expand coverage beyond what was physically recorded.

Synthetic Data Production at Scale

Asset-accurate synthetic generation using the same object meshes present in the real corpus.

Multi-Sensor Simulation

RGB, depth, IMU and contact signals rendered consistently across modalities.

Corner-Case Mining

Automated search for rare conditions that under-sample in natural collection.

Simulation Replay & Validation

Physics replay to reject dynamically implausible generated sequences before they enter training.

Training Data for World Models and Generative Video

Core Capabilities

World-Model-Driven Scene Generation

Synthetic Data Production at Scale

Multi-Sensor Simulation

Corner-Case Mining

Simulation Replay & Validation

Datasets Behind This Solution

MOVAS-Ego 100K

MOVAS-4D Multi-View

MOVAS-3D Assets

Other Solutions

Embodied Intelligence

Applied Physical AI

World Models & GenAI — Frequently Asked Questions

Precision Data. Infinite Momentum.