AI video motion continuity test: Inspect motion through time and occlusion

An AI video motion continuity test examines whether the subject remains understandable as it moves through time. A single attractive frame cannot establish continuity. Use a planned sequence of observations and normal-speed playback to identify changes in identity, trajectory, and contact that affect the intended clip.

Freeze the action brief

Write a short description of the start state, main action, and end state. Keep camera movement separate so a change in screen position is not automatically interpreted as subject motion.

For a fictional clip, a toy car starts beside a cup, travels behind it, and reappears on the other side. The continuity checks include which side it enters, whether the same car reappears, and whether its direction remains consistent. This is a test brief, not a claim about any model’s performance.

Use a duration allowed by the selected provider. The current Seedance schema accepts integer seconds from three through fifteen, Kling accepts five or ten, and Veo accepts eight. A prompt requesting a different timeline does not change the actual validated duration.

Mark temporal landmarks

Choose observations at the start, before an important transition, during the transition, after it, and at the end. Add observations around occlusion or contact because those moments can hide identity changes.

At each landmark, record subject position, orientation, visible distinguishing details, and relevant scene relationships. Note uncertainty when the subject is legitimately hidden. Do not fill invisible intervals with assumptions based only on a plausible final frame.

Inspect the same output file throughout the review. A preview assembled from different candidates cannot demonstrate one continuous action.

Combine frame and playback evidence

Watch at normal speed first to capture the viewer’s overall experience. Then inspect suspicious intervals slowly or frame by frame. Return to normal-speed playback after identifying an issue to judge its effect on the deliverable.

Look for sudden scale changes, reversed travel, duplicated subjects, disappearing components, and inconsistent interaction with surfaces. Distinguish an intentional edit from an unintended jump if the brief permits cuts.

Do not convert this checklist into a physical-accuracy percentage without a defined measurement procedure and real observations. The outcome is a documented editorial decision for the specified action.

Acceptance and storage

Set hard failures before reviewing, such as a changed subject identity or a teleport across a visible obstacle. A clip may still be acceptable for an abstract concept while failing a product demonstration; record the intended use.

Save time-coded notes, representative frames, the complete video, request provenance, and final reviewer decision. Continue with camera movement testing, frame artifact review, and single-shot prompting when separating motion intent from image quality.

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