Reference-Free Assessment of Physical Consistency in World Model-based Video Generation
We introduce reference-free measures for evaluating the physical consistency of generated videos, combining relative and absolute approaches to assess fidelity. Although tools like WorldGym or WorldEval enable robotic simulation via video generation, physical fidelity gaps often prevent these environments from accurately reproducing real-world task success rates of VLA models. Unlike existing evaluation methods, which require costly human voting (Elo) or unavailable ground-truth references (FVD), our approach utilizes DROID-SLAM and SEA-RAFT to quantify physical inconsistencies, motivated by WorldScore. Videos filtered using our relative consistency assessment show an improvement in task ...
| Authors | Yun Oh, Sukmin Yun |
|---|---|
| Primary category | cs.AI |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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