Identity drifts in a crowded full-body scene, with a narrower jawline and a thinner body making the result read as a similar-looking person rather than the same subject.

⚠ Struggled🧾 artifact-verifiedoutput onlyTest date not recordedScenario
What was measured
Identity preservation

How closely the generated face matches the reference image across scenes.

decisive for this rankingtransformation

This is the core of the task: the tool must keep the same character looking like the reference image across scenes. (3 of 3 judges)

What was given, what came back

Test input: Three-quarter face portrait · image
Input — what we sent
Input not captured
This run recorded no prompt or input file for the test, so we cannot show you what produced the result below. Capture gaps are tracked, not hidden.

Three-quarter face reference image with medium-dark skin, tight curly hair in an updo, bindi, and a floral dress. The partial angle and softer lighting make it a harder reference than the frontal portrait and are meant to stress identity consistency.

Why this input is hard
  • · Identity preservation with partial face angle
  • · Hair texture and updo retention
  • · Skin tone fidelity
  • · Reference-image difficulty under softer lighting
Output — unretouched
Output 1
Output 1
Output 2
Output 2
Provenance
Observation
6afd9a45-ca4d-4336-9bf1-660ad4e41244
Evidence run
1dfb8fa4-f7f0-47dc-911b-7db3af467e9a
Study
Generate Consistent AI Characters Across Different Scenes and Poses
Research task
86b96df11
Tested at
not recorded
Source
first-party
Evidence state
verified
Proof shown
output only
Cost / latency
not captured
Repeat run
not captured
Tester
not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this
get_evidence({
  tool: "scenario"
})
MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 4 other tools
measured on Identity preservation
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com