It converts a 3840×2160 landscape source into a 720×1280 portrait export, changing orientation as well as downscaling.

✗ Failed🧾 artifact-verifiedinput + output shownTest date not recordedDescript →
What was measured
Output resolution

Does it maintain the input resolution or downscale?

context, not decisivetransformation

Keeping full resolution is important for delivery quality, but it is a downstream output constraint rather than the main background-removal task itself. (3 of 3 judges)

What was given, what came back

Test input: Indoor Talking Head · video · group: remove-or-replace-video-backgrounds-using-ai
Input — what we sent
Indoor Talking Head

An indoor talking-head video of a person speaking directly to the camera with a static background, natural hand gestures, and facial expressions. It was used to test how well tools preserve the subject while replacing an indoor background.

Why this input is hard
  • · Static camera performance
  • · Face and body segmentation
  • · Facial expression preservation
  • · Background replacement accuracy in an indoor environment
Output — unretouched
Provenance
Observation
74e15c82-514d-483b-bce2-15ba2a3e9488
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 — 5 other tools
measured on Output resolution
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com