Game Environment Dataset
High-fidelity video from game engines with pixel-perfect ground truth for pre-training vision models, world models, and sim-to-real transfer.
Dataset at a Glance
Comparison with Public Datasets
How Claru's dataset compares to publicly available alternatives.
| Dataset | Clips | Hours | Modalities | Environments | Annotations |
|---|---|---|---|---|---|
| SYNTHIA | 13K | ~5 | RGB-D (syn) | Urban driving sim | Segmentation |
| Virtual KITTI | 21K | ~2 | RGB-D (syn) | Driving | Everything (GT) |
| Claru Game Environments | 66K+ | 450+ | RGB, Depth, PC | 50+ environments | Perfect GT: depth, seg, flow, normals, poses |
Use Cases
Vision Pre-Training
Massive supervised pre-training on perfect labels before real-data fine-tuning. Example models: DINOv2, SigLIP, InternImage.
World Model Pre-Training
Physically plausible environments for initializing world models. Example models: Genie 2, UniSim, DIAMOND.
Sim-to-Real Transfer
Pre-training on synthetic data reduces real-data requirements by 50-80%. Example models: Domain Randomization, RCAN, Transfer from Play.
How Claru Delivers This Data
Claru curates game data from 50+ virtual worlds with UE5/Unity fidelity. All data includes pixel-perfect ground truth annotations impossible to produce manually.
Frequently Asked Questions
Unreal Engine 5, Unity HDRP, and custom pipelines across 50+ environments with PBR materials.
Extracted from rendering engine: depth from Z-buffer, segmentation from object IDs, flow from motion vectors.
Best as pre-training, reducing real-data needs by 50-80% when combined with real-world fine-tuning.
Request a Sample Pack
Get a curated sample of game environment data with full annotations to evaluate for your project.