First-to-Last-Frame Face Drift in NSFW AI Video
Focus query: NSFW AI video face drift
This open worksheet turns a narrow adult-AI quality question into a reproducible review. It uses only synthetic adult identities and separates observed behavior from product claims. A reviewer should publish the complete denominator, failed cases, account tier, date, and configuration rather than reporting only a favorable example.
Research question
Does a fictional adult character remain the same person from the first frame to the final frame under motion and camera changes?
Controlled setup
Use an approved synthetic adult reference image and a short shot plan. Freeze duration, resolution, model, motion level, camera instruction, and prompt version.
Test protocol
- Capture frames at 0%, 25%, 50%, 75%, and 100% of the clip.
- Score face geometry, age presentation, wardrobe anchors, limb integrity, background continuity, and camera intent.
- Repeat once with camera motion removed to isolate motion-induced drift.
- Repeat once with character motion reduced to isolate pose-induced drift.
- Log the first failing timestamp and the variable most likely to have triggered it.
Scoring rubric
| Metric | Operational definition | Record |
|---|---|---|
| Identity continuity | Stable identity across sampled frames | 0-4 plus written evidence |
| First-failure time | Earliest timestamp with material drift | 0-4 plus written evidence |
| Age stability | Adult presentation remains clear in every sampled frame | 0-4 plus written evidence |
| Motion integrity | No material anatomy or wardrobe break under motion | 0-4 plus written evidence |
| Prompt fidelity | Shot and camera instructions remain recognizable | 0-4 plus written evidence |
Use 0 for a material failure, 1 for mostly failing, 2 for mixed behavior, 3 for a repeatable pass with minor defects, and 4 for a repeatable pass with clear evidence. Never replace the raw count with the average score.
Downloadable evidence schema
Download the blank CSV test template. Each row records a case ID, stage, controlled variable, expected result, observed result, metric, numeric score, evidence reference, hard-failure flag, and reviewer note. The file is intentionally blank: it is a protocol asset, not a fabricated benchmark.
Hard-failure rule
A hard failure occurs when identity changes materially, adult age presentation becomes ambiguous, or a late-frame defect is hidden by averaging stronger early frames.
Publication checklist
- State who ran the test and disclose any relationship with the product.
- Publish the test date, account tier, product mode, prompt or character version, and number of attempted cases.
- Keep failed examples and explain exclusions.
- Separate product facts, reviewer observations, and interpretations.
- Use synthetic adult identities; do not test with real-person likenesses or private personal data.
- Retest after model, memory, policy, pricing, or media-pipeline changes.
How to interpret the result
A passing score answers only the research question under the recorded conditions. It does not establish that a product is universally better, fully private, completely uncensored, or appropriate for every use. Compare methods and evidence before comparing averages. When two products use different account tiers or generation modes, report them as separate conditions rather than forcing a single ranking.
FAQ
Is this an independent Ponys.ai review?
No. This is a transparent, team-maintained test method. Independent publishers may reuse the blank protocol and should disclose their own methods and relationships.
Can the score be used as a marketing claim?
Only with the test date, denominator, configuration, and evidence. A number without those fields is not reproducible.
Why use synthetic adult cases?
They reduce privacy and likeness risk while making expected facts, identities, and boundaries explicit.
What should be cited?
Cite the protocol URL for the method and the publisher's own dated results page for observed findings. Do not cite the blank CSV as if it contained measured results.