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DTSTAMP:20240214T070241Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_626@linklings.com
SUMMARY:SAME: Skeleton-Agnostic Motion Embedding for Character Animation
DESCRIPTION:Technical Papers\n\nSunmin Lee, Taeho Kang, and Jungnam Park (
 Seoul National University); Jehee Lee (NC Research, Seoul National Univers
 ity); and Jungdam Won (Seoul National University)\n\nLearning deep neural 
 networks on human motion data has become common in computer graphics resea
 rch, but the heterogeneity of available datasets poses challenges for trai
 ning large-scale networks. \nThis paper presents a framework that allows u
 s to solve various animation tasks in a skeleton-agnostic manner. The core
  of our framework is to learn an embedding space to disentangle skeleton-r
 elated information from input motion while preserving semantics, which we 
 call Skeleton-Agnostic Motion Embedding (SAME). To efficiently learn the e
 mbedding space, we develop a novel autoencoder with graph convolution netw
 orks, and we provide new formulations of various animation tasks operating
  in the SAME space. We showcase various examples, including retargeting, r
 econstruction, and interactive character control, and conduct an ablation 
 study to validate design choices made during development.\n\nRegistration 
 Category: Full Access, Enhanced Access, Trade Exhibitor, Experience Hall E
 xhibitor
URL:https://asia.siggraph.org/2023/full-program?id=papers_626&sess=sess209
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