Free human-review protocol
Face swap quality scorecard for photos, videos, and GIFs
Rate one generated output with seven visible criteria, three critical publication gates, and a transparent weighted formula. The scorecard runs in this browser and exports the complete review as JSON or CSV.
Direct answer
A face swap quality score needs visible criteria and an evidence boundary
This scorecard structures a human review of one specific output. It does not inspect a face biometrically, predict an unseen result, or turn one favorable sample into a claim about an entire model or provider.
Interactive evaluation
Score one output with the same standard
Choose the media type, complete the gates, rate every applicable criterion, and keep a short evidence note for anything another reviewer should be able to find.
Rating anchors
Use the same 0-to-4 meaning for every criterion
The scale describes the strongest defect observed in the sampled material for that criterion. It is not a confidence score, probability, or biometric measurement.
| Rating | Anchor | Definition |
|---|---|---|
| 0 | Unusable | A failure or severe defect prevents the intended use. |
| 1 | Major defect | A clearly visible defect dominates normal viewing. |
| 2 | Noticeable defect | A defect remains easy to notice and needs targeted correction. |
| 3 | Minor defect | A small defect is visible on review but does not dominate normal viewing. |
| 4 | No material defect observed | No material defect was observed in the sampled output for this criterion. |
Rubric dimensions
Weights are explicit and motion is only scored when motion exists
Video and GIF use all 100 base-weight points. Photo excludes temporal stability and normalizes the remaining 90 points to a 100-point result.
| Criterion | Base weight | Media | Review question |
|---|---|---|---|
| Identity preservation | 24% | photo, video, gif | Do the visible eyes, brows, nose, mouth, jaw, age cues, and overall identity remain coherent with the intended reference? |
| Face boundary and blend | 16% | photo, video, gif | Do skin texture, face edges, ears, jaw, hairline, and neck transition into the target scene without a pasted-on boundary? |
| Pose and expression coherence | 14% | photo, video, gif | Does facial geometry remain coherent with the target head angle, gaze, eye state, mouth shape, and expression? |
| Lighting and color continuity | 14% | photo, video, gif | Do exposure, color, skin shading, highlights, and shadows remain consistent with the target scene? |
| Occlusion and accessory continuity | 12% | photo, video, gif | Do hair, hands, glasses, masks, microphones, foreground objects, and other occlusions stay in the correct visual order? |
| Technical integrity | 10% | photo, video, gif | Is the output free from material blur, ringing, block artifacts, tearing, duplicate features, abrupt texture changes, or damaged frames? |
| Temporal stability | 10% | video, gif | Across motion, does identity remain stable without flicker, drift, face loss, sudden geometry changes, or a visible loop seam? |
Download the reusable protocol
The JSON contains the complete scale, gates, criteria, formula, decision bands, evidence limits, license, and references. The CSV is a blank seven-row review template.
Five-step evaluation protocol
Make each review reproducible enough for another person to inspect
- Choose the output type and identify the sample. Select photo, video, or GIF and record a sample identifier, reviewer, and review date.
- Confirm the three critical publication gates. Confirm permission, verify the intended identity mapping, and review whether an AI-content disclosure is needed.
- Review every applicable criterion. Rate identity, face boundary, pose and expression, lighting, occlusion, technical integrity, and temporal stability where applicable.
- Read the weighted result without overriding the gates. Use the result to prioritize corrections. A numerical score never overrides unresolved permission, mapping, or disclosure gates.
- Export the complete evidence record. Download JSON or CSV, retain notes, and repeat the same protocol for each output you intend to compare.
Provider comparisons need more evidence: use identical permitted inputs, repeated independent runs, multiple reviewers, documented viewing conditions, and statistical reporting. One hand-picked output cannot establish average model quality.
Evidence boundary and sources
Subjective review is useful only when its limits are visible
Boundary
The score is a structured human observation of one reviewed output, not a biometric identity measurement.
Boundary
The rubric does not establish model accuracy, average quality, safety, speed, reliability, or provider superiority.
Boundary
A high score does not replace consent, disclosure, legal, accessibility, or audience-specific review.
Boundary
A cross-provider claim requires the same inputs, repeated runs, independent reviewers, documented viewing conditions, and statistical reporting.
- ITU-R BT.500-15: Methodologies for the subjective assessment of the quality of television images - General principles for documented subjective image assessment; the DeepSwapAI rubric is not an ITU laboratory test.
- ITU-T P.910 (10/2023): Subjective video quality assessment methods for multimedia applications - General principles for documented subjective multimedia assessment; the DeepSwapAI rubric is a practical single-review workflow.
- DeepSwapAI controlled input-readiness research - separate input-only evidence with no generated-output score.
- DeepSwapAI claim verification methodology - how product facts, comparisons, and evidence boundaries are reviewed.
Scorecard questions
What the number can and cannot establish
Does this upload my media or notes?
No. The page has no media input, and scorecard controls do not send ratings or notes to DeepSwapAI.
Is this a biometric identity score?
No. It is a documented human observation of visible output characteristics, not face-recognition accuracy or embedding similarity.
Can one score rank face swap providers?
No. A defensible comparison needs shared inputs, repeated outputs, multiple reviewers, controlled viewing conditions, and statistical reporting.
Why is temporal stability excluded from photos?
A still image has no frame sequence. Photo mode normalizes the six applicable criteria from 90 base-weight points to 100.
Can I reuse the rubric?
Yes. The published JSON and blank CSV use CC BY 4.0 and include the required attribution statement.
Generate the smallest representative output first
Choose one permitted target and identity reference, generate one representative result, then return to score it before scaling a batch or long clip.