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-verifiedinput + output shownTest 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
Three-quarter face portrait
Three-quarter face portrait

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
input + output shown
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