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DTSTAMP:20240214T070245Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T174500
DTEND;TZID=Australia/Melbourne:20231213T175500
UID:siggraphasia_SIGGRAPH Asia 2023_sess167_papers_626@linklings.com
SUMMARY:SAME: Skeleton-Agnostic Motion Embedding for Character Animation
DESCRIPTION:Technical Communications, Technical Papers\n\nSunmin Lee, Taeh
 o Kang, and Jungnam Park (Seoul National University); Jehee Lee (NC Resear
 ch, Seoul National University); and Jungdam Won (Seoul National University
 )\n\nLearning deep neural networks on human motion data has become common 
 in computer graphics research, but the heterogeneity of available datasets
  poses challenges for training large-scale networks. \nThis paper presents
  a framework that allows us to solve various animation tasks in a skeleton
 -agnostic manner. The core of our framework is to learn an embedding space
  to disentangle skeleton-related information from input motion while prese
 rving semantics, which we call Skeleton-Agnostic Motion Embedding (SAME). 
 To efficiently learn the embedding space, we develop a novel autoencoder w
 ith graph convolution networks, and we provide new formulations of various
  animation tasks operating in the SAME space. We showcase various examples
 , including retargeting, reconstruction, and interactive character control
 , and conduct an ablation study to validate design choices made during dev
 elopment.\n\nRegistration Category: Full Access\n\nSession Chair: Yoonsang
  Lee (Hanyang University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_626&sess=sess167
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