Is Your Driving World Model an All-Around Player?

• URL: http://arxiv.org/abs/2605.10858v1
• Date: Mon, 11 May 2026 17:05:49 GMT
• Title: Is Your Driving World Model an All-Around Player?
• Abstract summary: We introduce WorldLens, a unified benchmark that measures world-model fidelity across the full spectrum.<n>Our evaluation of six representative models reveals that no existing approach dominates across all axes.<n>To bridge algorithmic metrics with human perception, we contribute WorldLens-26K, a 26,808-entry human-annotated preference dataset.
• Score: 107.28582946012818
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic physics; others maintain geometric consistency but fail when subjected to closed-loop planning. This disconnect exposes a critical gap: the field evaluates how real generated worlds appear, but rarely whether they behave realistically. We introduce WorldLens, a unified benchmark that measures world-model fidelity across the full spectrum, from pixel quality and 4D geometry to closed-loop driving and human perceptual alignment, through five complementary aspects and 24 standardized dimensions. Our evaluation of six representative models reveals that no existing approach dominates across all axes: texture-rich models violate geometry, geometry-aware models lack behavioral fidelity, and even the strongest performers achieve only 2-3 out of 10 on human realism ratings. To bridge algorithmic metrics with human perception, we further contribute WorldLens-26K, a 26,808-entry human-annotated preference dataset pairing numerical scores with textual rationales, and WorldLens-Agent, a vision-language evaluator distilled from these judgments that enables scalable, explainable auto-assessment. Together, the benchmark, dataset, and agent form a unified ecosystem for assessing generated worlds not merely by visual appeal, but by physical and behavioral fidelity.

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