Direct answer
Audit AI avatar identity drift by comparing each new image against the approved model sheet, not against memory or taste. Check facial structure, age, proportions, palette, wardrobe logic, expression, role fit, and whether the image still reads as the same character.
Why this matters
The common advice is “keep your character consistent”, but that is not an audit. Teams need objective drift checks, thresholds and rejection rules so they can decide when a generation is usable.
How to use it
- Start with the approved model sheet.
- Compare fixed identity markers first: face, age, body proportions, key features.
- Then compare style markers: palette, lighting, illustration/render style and framing.
- Check role fit: does the avatar still perform the intended brand or content job?
- Mark outputs as approve, adjust, regenerate or retire.
Example
If the avatar’s hairstyle and outfit vary but the face, age, glasses, posture and palette remain stable, the output may pass. If the face shape or apparent age changes, it should fail even if the image looks polished.
Caveats and limits
Some drift is useful when it supports context. The problem is not variation; it is uncontrolled variation in identity-defining traits.