Beyond Semantic Understanding: Physics-Informed Adaptation of Video Foundation Models for Precision Industrial Metrology

Video foundation models (VFMs) pretrained on internet-scale data have demonstrated remarkable generalization in semantic segmentation and object tracking. However, we identify a critical limitation when these models are deployed for quantitative industrial analysis: semantic understanding does not entail metrological precision. A VFM may correctly segment a rotating mechanical component while producing kinematic measurements that violate rigid-body constraints—a deficiency we term the semantic-precision gap. Through controlled experiments, we establish that this gap is structural: it persists under stronger backbones (SAM 3), parameter-efficient fine-tuning (LoRA, Adapters), and state-of-the-art video object segmentation (Cutie, DEVA), because none introduce the physical inductive biases necessary for geometric and kinematic reasoning.