The 3D run is described as having strong cinematic depth and realism, and version 2 is said to be more refined and more immersive.
What was measured
Cinematic Enhancement (camera, environment, effects)
How effectively the output adds cinematic camera movement, environmental motion, and visual effects to the source image.
decisive for this rankingtransformation
The ranking is specifically about making a video cinematic, so camera movement, environmental motion, and effects are part of the core outcome. (3 of 3 judges)
What was given, what came back
Test input: 3D rendered street scene image with forward dolly prompt · image · group: image-to-cinematic-video
Input — what we sent

3D rendered street scene image with forward dolly prompt
A rendered 3D street scene with multiple characters and environment detail, tested using a forward-dolly cinematic prompt with crowd motion, cart movement, atmospheric haze, and sunset lighting.
Why this input is hard
- · Complex multi-subject scene animation
- · Character consistency in crowd motion
- · Camera movement stability during forward dolly
- · Environmental motion such as clouds, trees, and birds
- · Prompt adherence across foreground and background elements
Also checked on this input — same tool, 3 other criteria
Motion Quality & Realism✓ WorkedThe 3D run shows natural walking and character interaction with smooth motion.Sound Design✓ WorkedVersion 2 of the 3D run includes sound, showing that the tool can attach audio to an output variant.Visual Consistency (No Distortion)✓ WorkedThe 3D run shows no visible distortion in either version, according to the report.
Provenance
- Observation
- 4601892d-e472-4339-96a0-061f11e8d16f
- Evidence run
- b8778c3c-4fb1-48b2-bf35-4e08a3b82d8e
- Study
- Generate a cinematic AI video from a single image
- Research task
- 86b94urgr
- 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: "luma-ai",
scenario: "image-to-cinematic-video"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 2 other tools
measured on Cinematic Enhancement (camera, environment, effects)
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com