Abstract
AI-generated video often fails in time rather than in any single frame. Typical failures are global brightness flicker, frozen segments, padded duplicate frames, high-frequency texture “boil”, and abrupt discontinuities. Learned quality models can score such clips, but they need GPU inference, pinned checkpoints, and feature extractors that change between releases, which makes results hard to reproduce outside the original lab. This paper proposes TSM, a training-free Temporal Stability Metric suite that needs only FFmpeg and array arithmetic. TSM computes six interpretable measures on downsampled luma: activity, freeze ratio, flicker index, temporal jitter ratio, a flash-robust shot-boundary count, and a native-rate duplicate-frame ratio. It also defines a frame-difference time series that serves as a temporal signature for detecting re-encoded or re-framed variants of the same clip. We validate TSM in three ways. First, controlled perturbations of 15 clean clips show that each target metric responded in the expected direction in 15 of 15 clips for freezes, flicker, frame duplication, and temporal noise, and in 14 of 15 for an inserted hard cut. Second, on 57 candidate shot boundaries annotated by visual inspection, the TSM detector reached precision 0.756 and recall 0.944, against 0.875 and 0.972 for FFmpeg's scdet; requiring both detectors gave 0.917 for both. Third, re-encoding at CRF 35 changed every continuous metric by a median of at most 3.4%. We then apply TSM to 35 public showcase files from three AI video websites (Section 6). Checksums show that 10 files are byte-identical across two sites, which leaves 25 unique clips, and the temporal signature identifies one portrait/widescreen variant pair. The case study shows how TSM separates multi-shot edits from single-shot clips before any quality comparison. It does not rank the sites or the generators behind them.