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BEGIN:VEVENT
DTSTAMP:20240214T070311Z
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:Technical Communications, Technical Papers\n\nLive4D: A Real-t
 ime Capture System for Streamable Volumetric Video\n\nWe present Live4D, a
  cost-effective volumetric video capture and streaming system based on RGB
 -only camera setup, which enables real-time generation of temporally stabl
 e and watertight meshes.\n\n\nYifeng Zhou, Shuheng Wang, Wenfa Li, Chao Zh
 ang, Li Rao, Pu Cheng, Yi Xu, Jinle Ke, Wenduo Feng, Wen Zhou, Hao Xu, Yuk
 ang Gao, Yang Ding, Weixuan Tang, and Shaohui Jiao (ByteDance)\n----------
 -----------\nDiscovering Fatigued Movements for Virtual Character Animatio
 n\n\nVirtual character animation and movement synthesis have advanced rapi
 dly during recent years, especially through a combination of extensive mot
 ion capture datasets and machine learning. A remaining challenge is intera
 ctively simulating characters that fatigue when performing extended motion
 s, which ...\n\n\nNoshaba Cheema (German Research Center for Artificial In
 telligence, Max-Planck Institute for Informatics); Rui Xu (German Research
  Center for Artificial Intelligence, Saarland University); Nam Hee Kim and
  Perttu Hämäläinen (Aalto University); Vladislav Golyanik and Marc Haberma
 nn (Max-Planck-Institut für Informatik); Christian Theobalt (Max-Planck-In
 stitut für Informatik, Saarland University); and Philipp Slusallek (Saarla
 nd University, German Research Center for Artificial Intelligence)\n------
 ---------------\nEasyVolcap: Accelerating Neural Volumetric Video Research
 \n\nEasyVolcap is a Python & PyTorch library for accelerating volumetric v
 ideo research, particularly in the area of neural dynamic scene representa
 tion, reconstruction and rendering.\n\n\nZhen Xu (State Key Lab of CAD and
  CG, Zhejiang University; Zhejiang University); Tao Xie (State Key Lab of 
 CAD\&CG, Zhejiang University); Sida Peng (Zhejiang University); Haotong Li
 n (State Key Lab of CAD\&CG, Zhejiang University); Qing Shuai (Zhejiang Un
 iversity); Zhiyuan Yu (Department of Mathematics, Hong Kong University 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---------------------\nACE: Adversarial Correspondence Embedding for Cros
 s Morphology Motion Retargeting from Human to Nonhuman Characters\n\nMotio
 n retargeting is a promising approach for generating natural and compellin
 g motions for nonhuman characters. However, it is challenging to translate
  human movements into semantically equivalent motions for target character
 s with very different morphologies due to ambiguity. This work presents a.
 ..\n\n\nTianyu Li (Georgia Institute of Technology), Jungdam Won (Seoul Na
 tional University), Alexander Clegg (Meta), Jeonghwan Kim (Georgia Institu
 te of Technology), Akshara Rai (Meta), and Sehoon Ha (Georgia Institute of
  Technology)\n---------------------\nSAME: Skeleton-Agnostic Motion Embedd
 ing for Character Animation\n\nLearning deep neural networks on human moti
 on data has become common in computer graphics research, but the heterogen
 eity of available datasets poses challenges for training large-scale netwo
 rks. \nThis paper presents a framework that allows us to solve various ani
 mation tasks in a skeleton-agnostic ...\n\n\nSunmin Lee, Taeho Kang, and J
 ungnam Park (Seoul National University); Jehee Lee (NC Research, Seoul Nat
 ional University); and Jungdam Won (Seoul National University)\n\nRegistra
 tion Category: Full Access\n\nSession Chair: Yoonsang Lee (Hanyang Univers
 ity)
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