Presentation

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TEDi: Temporally-Entangled Diffusion for Long-term Motion Synthesis
SessionCharacter Animation From Data
DescriptionTEDi introduces a novel approach to long-term motion synthesis by entangling the denoising diffusion time-axis with the temporal motion time-axis. This is achieved through temporally varying denoising, allowing TEDi to generate high-quality, diverse motions auto-regressively. This new mechanism paves the way towards a new framework for long-term motion synthesis.
Authors
Zihan Zhang
University of Chicago
Richard Liu
University of Chicago
Rana Hanocka
University of Chicago
Kfir Aberman
Snap
Event Type
Technical Paper
TimeWednesday, 31 July 202411:05am - 11:15am MDT
LocationMile High 4
ACM Digital Library Journal Papers' PDFs
Conference Papers' PDFs
Session Time & Location
Sunday, 28 July 20246:00pm - 8:45pm MDTBluebird Ballroom
Wednesday, 31 July 202410:45am - 12:15pm MDTMile High 4
Interest Areas
Research & Education
Recordings
Livestreamed
Recorded
Keywords
Animation
Registration Categories
Full Conference
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Exhibitor Full Conference
Wednesday
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