data_entry_ops · media · workflow
How Move.ai is revolutionizing content creation with better video data
Move.ai needed to rapidly train multiple AI algorithms for markerless motion capture and depth keying, requiring labeled video data at speed; their models had to identify people, jersey numbers, and human limbs across varied distances, poses, and occlusions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Training data need identified
Move.ai turned to Labelbox to spin up a labeling team, develop training data for their models, and iterate at top speed.
Tools used
LabelboxBoostPython SDKTensorflowPyTorchTensorRTNvidia RTX GPUsNvidia T4s
Outcome
Labelbox's data engine enabled Move.ai to iterate twice as fast on their algorithms, accelerating their go-to-market strategy and product launches.
Results
Volume2x as fast
Grounding & classification
Source type: vendor customer story
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