DamEQ-Bench: A Physics-Aware Benchmark for Gravity-Dam Seismic Video Generation

Generating physically plausible seismic-response videos of gravity dams remains challenging for modern video generation models. Unlike active motion in natural videos, gravity-dam vibration is a passive forced response governed by the prescribed seismic excitation, reservoir condition, and structural state; consequently, visual realism does not necessarily imply physically correct dynamics. To address this gap, we introduce DamEQ-Bench, a comprehensive benchmark for state-conditioned gravity-dam seismic video generation. DamEQ-Bench is constructed from controlled large-scale shaking-table experiments and comprises 198 seismic conditions spanning empty-reservoir and impounded-reservoir states, together with approximately 1,300 multi-view UAV recordings and synchronized sensor measurements as experimental references. We formulate gravity-dam seismic video generation as visually conditioned I2V/V2V tasks driven by prescribed seismic conditions and an initial image or video. To jointly assess visual quality and physical-response fidelity, we establish a comprehensive evaluation framework combining six general visual-quality metrics with eight response-oriented metrics that characterize structural dynamic response and modal characteristics. Under a unified protocol, we further evaluate sixteen representative open- and closed-source video generation models, together with two non-generative static control baselines, and investigate whether lightweight LoRA-based domain adaptation can improve seismic-response fidelity. DamEQ-Bench provides a unified, interpretable, and reproducible testbed for distinguishing visually plausible motion from structural responses that are consistent with prescribed seismic conditions.