Segment Continuity measures whether a generated video contains detected scene cuts. Unintended hard transitions break temporal coherence and indicate segment discontinuities.
Its scope is scene_cut_detection_only. A no-cut result does not establish object identity, spatial-layout consistency or absence of motion freezes; use those abilities’ own measurements separately.
TransNetV2 outputs a per-frame scene-boundary probability pi for decoded frame xi. Frames with pi>θ are flagged as cut candidates, with defaults θ=0.5. A cut is recorded at frame index i when the binary mask rises from 0 to 1 at i, following TransNetV2 transition parsing.
To suppress spurious detections, cut candidates are filtered so that every retained scene spans at least L frames (default L=10). Let ncut denote the number of retained cuts in one video. The per-video score is binary:
Sseg={1,0,ncut=0ncut>0(1)
Across N evaluated videos, the model-level aggregate is
Sˉseg=1−Nncut(2)
where ncut counts videos with at least one retained cut. WorldAtlas stores the per-sample binary value from Eq. (1) as the metric raw score. The metric is prediction-only and requires TransNetV2 inference code and weights.
Camera pull out. A rocky shoreline with trees and distant hills. Camera move right. A forest edge with dense foliage and sunlight. Camera pan right. A forest edge with forest path, scattered rocks, and tree canopy.
prompt source: prompt_target
Both first frames passed geometric matching against the displayed input; labels compare this metric only.
Model result videos will be available with the ModelScope release. / 模型推理视频将在 ModelScope 方案上线后提供。
Higher metric scoreScore 1.0000Detected hard cuts: 0
Model result videos will be available with the ModelScope release. / 模型推理视频将在 ModelScope 方案上线后提供。
Lower metric scoreScore 0.0000Detected hard cuts: 1