The warm lantern-lit alley does not wrap light onto the subjects: jacket edges still read as flat daylight with no warm rim light.
What was measured
Lighting adaptation
Does the replaced background look naturally lit relative to the subject, or obviously composited?
decisive for this rankingtransformation
If the replaced background does not match the subject’s lighting, the composite looks fake, so this is central to replacement quality. (3 of 3 judges)
What was given, what came back
Test input: Busy Urban Street · video · group: remove-or-replace-video-backgrounds-using-ai
Input — what we sent
Busy Urban Street
A video of a person walking through a busy city street with multiple people, moving vehicles, and a visually complex urban background. It was used to stress segmentation and tracking under heavy background distraction and continuous motion.
Why this input is hard
- · Complex background segmentation
- · Handling background distractions
- · Subject tracking
- · Temporal consistency across frames
- · Edge accuracy during continuous movement
Output — unretouched

Also checked on this input — same tool, 2 other criteria
Provenance
- Observation
- 87e47faa-5505-4f29-95c7-726fa54b39c2
- Evidence run
- 10155228-7ce8-438a-a210-547331ef080b
- Study
- Remove or Replace Video Backgrounds Using AI
- Research task
- 86ba42c2d
- 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: "descript",
scenario: "remove-or-replace-video-backgrounds-using-ai"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 1 other tool
measured on Lighting adaptation
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com