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FTV1750 | From Capture to AI: How Data Enrichment Unlocks the Value of Motion
  • Wednesday, Sep 1612:30 PM - 1:00 PM PDT

AI-powered animation is only as good as the data behind it, but most motion data wasn’t designed for AI. In this session, we’ll demonstrate a projection-mapped capture app that guides performers in real time to produce more-structured motion. As well as how Label Maker, a tool integrated within the Autodesk ecosystem, uses AI to turn raw motion data and videos into richly tagged, reusable data sets. You will walk away with a clearer picture of how to make motion data actually usable for AI, and how to rethink their pipelines to get there. The real value of motion data isn’t in the capture—it’s in the enrichment.

Learning Objective 1: Discover gaps between raw motion data and AI-ready data sets, and where value is created or lost in pipelines.
Learning Objective 2: Learn about principles for systematic data capture to create comprehensive, well-organized motion data sets from the start.
Learning Objective 3: Learn about and evaluate how AI-assisted annotation reduces manual effort while producing structured data sets—AI proposes, human confirms.
Learning Objective 4: Learn how enriched data sets support AI uses—from motion generation to action recognition—via “label once, train anywhere.”
Format: Strategy Talk
Topic: Data Exchange and System Integrations
Industry: Film and TV
Product: Flow Production Tracking, Maya
Occupation: VFX Artist, Producer, Software engineer/developer, Technical director, Mocap artist
Industry Group Catalog Filter: M&E
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Speakers

AU Speakers
Co-Speakers
AIA Continuing education credits: AIA LU