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WMBench
Benchmark suite for evaluating hallucination / anomaly detection on video generated by world models (Cosmos, OpenSora, etc.) against real robot training distributions.
Datasets
| Dataset | Source | Tasks | Videos |
|---|---|---|---|
gr-1/ |
NVIDIA GR1 (PhysicalAI-Robotics-GR00T-GR1) |
5 eval tasks | 5 real + 24 Cosmos gens |
droid/ (planned) |
Stanford DROID | — | — |
Each dataset folder contains:
training/Real training mp4s (one per task)generated/World-model generated mp4s, grouped by taskreference/50 SAM3-segmented PNG frames per task (for null calibration)null_per_task/Pre-computed cycle null distributions (.npz)results/WarpDyn scores, ranking, visualizationsmethod.mdPer-dataset method notes / reproducibility
Method: WarpDyn
Pure feature-matching anomaly detector using RoMa cycle composition error, per-task multi-lag null distribution, and per-task ratio scoring.
See gr-1/method.md for the full step-by-step method.
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