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DTSTAMP:20260114T163644Z
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:Sunmin Lee, Taeho Kang, and Jungnam Park (Seoul National Unive
 rsity); Jehee Lee (NC Research, Seoul National University); and Jungdam Wo
 n (Seoul National University)\n\nLearning deep neural networks on human mo
 tion data has become common in computer graphics research, but the heterog
 eneity of available datasets poses challenges for training large-scale net
 works. \nThis paper presents a framework that allows us to solve various a
 nimation tasks in a skeleton-agnostic manner. The core of our framework is
  to learn an embedding space to disentangle skeleton-related information f
 rom input motion while preserving semantics, which we call Skeleton-Agnost
 ic Motion Embedding (SAME). To efficiently learn the embedding space, we d
 evelop a novel autoencoder with 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 i
 nteractive character control, and conduct an ablation study to validate de
 sign choices made during development.\n\nRegistration Category: Full Acces
 s\n\nSession Chair: Yoonsang Lee (Hanyang University)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_626&sess=sess167
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