Physical Plausibility Checks

Production deployments of generated video need automated guardrails. Human review does not scale; physics checks catch the classic failure modes.

When to use

• Any product that generates video from text or image prompts.
• Automating QA on a video generation API endpoint.
• Monitoring a video model's quality drift after fine-tuning or a base-model update.

Inputs

• video: a tensor (T, H, W, 3) or a path to an mp4.
• Optional reference info: expected number of objects, initial scene description.

Checks

1. Object permanence

Track every detection across frames with SAM 3.1 Object Multiplex. Flag when a stable track disappears for <=3 frames and reappears — the model lost the object temporarily. Hard fail when an object disappears near the frame centre (not at an edge); soft fail at edges.

2. Motion smoothness

Optical flow between consecutive frames should be mostly continuous. Sudden per-pixel flow spikes indicate teleportation. Compute flow with RAFT; flag frames where the 99th-percentile flow magnitude exceeds the median by a factor > 10.

3. Gravity / support

For objects detected as solid (food, balls, tools), check that their vertical position is non-increasing in the absence of a lifting action. Flag upward drift unless a "grasping hand" is detected near the object.

4. Identity consistency

For people or characters, use a face-recognition embedding across frames. Cosine similarity should stay > 0.8 across 5-frame windows for a persistent identity. Below threshold means the character morphed.

5. Hands and limbs

Run a pose estimator (Lesson 21). Flag frames where a hand has > 5 or < 4 visible fingers; where an arm length doubles between frames; where limbs intersect the body through a surface.

6. Text rendering (if prompt asked for text)

If the user prompt included a string in quotes, OCR the generated frames and compute CER against the requested string. Flag > 20% CER.

Report

[plausibility]
  video frames:           <T>
  permanence violations:  <N>
  smoothness violations:  <N>
  gravity violations:     <N>
  identity drift:         <N of 5-frame windows>
  limb anomalies:         <N>
  OCR CER vs requested:   <float>

[verdict]
  ship | hold | reject

[samples for review]
  frame ranges where each failure occurred

Rules

• Do not hard-block on any single check; aggregate scores and hold the video for review when total anomalies exceed a threshold.
• Weight identity drift and permanence violations highest — users notice them first.
• Log per-check failure rates over time; a rising trend usually means the base model was updated or the prompt distribution shifted.
• Never delete the flagged video; keep it for model debugging and post-mortems.
• For sensitive content (people, children, public figures), require human review of every video regardless of score.