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DTSTAMP:20260114T163737Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T174500
DTEND;TZID=Australia/Melbourne:20231213T183100
UID:siggraphasia_SIGGRAPH Asia 2023_sess167@linklings.com
SUMMARY:Motion Synthesis With Awareness, Part II
DESCRIPTION:SAME: Skeleton-Agnostic Motion Embedding for Character Animati
 on\n\nLearning deep neural networks on human motion data has become common
  in computer graphics research, but the heterogeneity of available dataset
 s poses challenges for training large-scale networks. \nThis paper present
 s a framework that allows us to solve various animation tasks in a skeleto
 n-agnostic ...\n\n\nSunmin Lee, Taeho Kang, and Jungnam Park (Seoul Nation
 al University); Jehee Lee (NC Research, Seoul National University); and Ju
 ngdam Won (Seoul National University)\n---------------------\nLive4D: A Re
 al-time Capture System for Streamable Volumetric Video\n\nWe present Live4
 D, a cost-effective volumetric video capture and streaming system based on
  RGB-only camera setup, which enables real-time generation of temporally s
 table and watertight meshes.\n\n\nYifeng Zhou, Shuheng Wang, Wenfa Li, Cha
 o Zhang, Li Rao, Pu Cheng, Yi Xu, Jinle Ke, Wenduo Feng, Wen Zhou, Hao Xu,
  Yukang Gao, Yang Ding, Weixuan Tang, and Shaohui Jiao (ByteDance)\n------
 ---------------\nACE: Adversarial Correspondence Embedding for Cross Morph
 ology Motion Retargeting from Human to Nonhuman Characters\n\nMotion retar
 geting is a promising approach for generating natural and compelling motio
 ns for nonhuman characters. However, it is challenging to translate human 
 movements into semantically equivalent motions for target characters with 
 very different morphologies due to ambiguity. This work presents a...\n\n\
 nTianyu Li (Georgia Institute of Technology), Jungdam Won (Seoul National 
 University), Alexander Clegg (Meta), Jeonghwan Kim (Georgia Institute of T
 echnology), Akshara Rai (Meta), and Sehoon Ha (Georgia Institute of Techno
 logy)\n---------------------\nEasyVolcap: Accelerating Neural Volumetric V
 ideo Research\n\nEasyVolcap is a Python & PyTorch library for accelerating
  volumetric video research, particularly in the area of neural dynamic sce
 ne representation, reconstruction and rendering.\n\n\nZhen Xu (State Key L
 ab of CAD and CG, Zhejiang University; Zhejiang University); Tao Xie (Stat
 e Key Lab of CAD\&CG, Zhejiang University); Sida Peng (Zhejiang University
 ); Haotong Lin (State Key Lab of CAD\&CG, Zhejiang University); Qing Shuai
  (Zhejiang University); Zhiyuan Yu (Department of Mathematics, Hong Kong U
 niversity of Science and Technology); Guangzhao He (State Key Lab of CAD\&
 CG, Zhejiang University); Jiaming Sun (Image Derivative Inc.); Hujun Bao (
 State Key Lab of CAD\&CG, Zhejiang University); and Xiaowei Zhou (Zhejiang
  University)\n---------------------\nDiscovering Fatigued Movements for Vi
 rtual Character Animation\n\nVirtual character animation and movement synt
 hesis have advanced rapidly during recent years, especially through a comb
 ination of extensive motion capture datasets and machine learning. A remai
 ning challenge is interactively simulating characters that fatigue when pe
 rforming extended motions, which ...\n\n\nNoshaba Cheema (German Research 
 Center for Artificial Intelligence, Max-Planck Institute for Informatics);
  Rui Xu (German Research Center for Artificial Intelligence, Saarland Univ
 ersity); Nam Hee Kim and Perttu Hämäläinen (Aalto University); Vladislav G
 olyanik and Marc Habermann (Max-Planck-Institut für Informatik); Christian
  Theobalt (Max-Planck-Institut für Informatik, Saarland University); and P
 hilipp Slusallek (Saarland University, German Research Center for Artifici
 al Intelligence)\n\nRegistration Category: Full Access\n\nSession Chair: Y
 oonsang Lee (Hanyang University)
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