UploadCheck for Luma AI (Dream Machine)
Your Luma AI (Dream Machine) generation, checked before your audience checks it.
Dream Machine's diffusion renders look stunning at a glance, then fall apart on the second watch: a face that morphs mid-shot, background detail that boils and flickers frame to frame, a hand that grows a sixth finger, or a loop whose seam jumps. UploadCheck watches every exported clip frame by frame and catches these generation artifacts — warping, temporal inconsistency, cloned subjects, audio desync, and broken loops — before you post them.
- Morphing / warping
- Temporal flicker
- Extra fingers / limbs
- Cloned subjects
- Loop seam jump
- Audio / lip-sync drift
FAQ
UploadCheck & Luma AI (Dream Machine)
How does UploadCheck work with Luma AI (Dream Machine)?
You generate and export your generation in Luma AI (Dream Machine) as usual, then run the finished file through UploadCheck — from the web, the CLI, or your AI assistant. UploadCheck scans it for problems Luma AI (Dream Machine) can't check, returns timestamped flags and fixes, and you re-check until it passes.
What problems does UploadCheck catch on a Luma AI (Dream Machine) generation?
Morphing and warped motion, temporal flicker, frozen/looped frames, cloned or duplicated subjects across a shot, audio that drifts out of sync with the video, off-target loudness, and wrong resolution/codec/frame-rate — each with the exact timestamp and a fix.
Do I need to change my Luma AI (Dream Machine) workflow?
No. UploadCheck is the last step after your Luma AI (Dream Machine) generation — one check before you publish. It doesn't touch your Luma AI (Dream Machine) project; it inspects the finished file and hands you (or your AI) a repair list.
Does UploadCheck catch AI generation artifacts from Luma AI (Dream Machine)?
UploadCheck doesn't reject a clip for being AI-generated — it inspects what's actually on the frames. Deterministic gates catch loop seams and freezes (loop_freeze), audio-to-motion desync (av_sync), and framing/letterbox problems (canvas_fill), while the paid AI oracle gates go further: twins flags a subject that morphs or clones itself across a scene, and the omni_watch and gemini_watch multimodal watchers see and hear the clip to flag the "something looks wrong" artifacts — boiling texture, warped anatomy, temporal instability — that Dream Machine's diffusion pipeline is prone to, then tell your AI exactly what to fix.
How much time does it save?
Instead of re-watching a clip to hunt for the one bad moment — or worse, finding it after you've published — UploadCheck flags it in one pass with the exact timestamp. Catching one bad generation before publish saves the re-generate/re-export, re-upload, and the reputational hit of a broken clip going live.