worldbench/WorldLens
[CVPR 2026] WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
WorldLens helps evaluate the true capabilities of AI models designed to simulate driving environments. It takes a driving world model as input and provides a comprehensive report across 24 dimensions, covering visual realism, geometric consistency, and functional reliability. Autonomous driving researchers and engineers can use this to understand how their models perform in real-world scenarios.
188 stars.
Use this if you are developing or testing AI models that generate realistic driving simulations and need a thorough, multi-faceted evaluation of their performance.
Not ideal if you are looking for a tool to train driving models or to generate synthetic data for general computer vision tasks unrelated to autonomous driving.
Stars
188
Forks
16
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 18, 2026
Commits (30d)
0
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