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