BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Australia/Melbourne
X-LIC-LOCATION:Australia/Melbourne
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:19721003T020000
RRULE:FREQ=YEARLY;BYMONTH=4;BYDAY=1SU
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:19721003T020000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260114T163729Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T154000
DTEND;TZID=Australia/Melbourne:20231215T163500
UID:siggraphasia_SIGGRAPH Asia 2023_sess139@linklings.com
SUMMARY:Motion Capture and Reconstruction
DESCRIPTION:Reconstructing Close Human Interaction from Multiple Views\n\n
 This paper addresses the challenging task of reconstructing the poses of m
 ultiple individuals engaged in close interactions, captured by multiple ca
 librated cameras. The difficulty arises from the noisy or false 2D keypoin
 t detection due to inter-person occlusion, the heavy ambiguity to associat
 e ke...\n\n\nQing Shuai (Zhejiang University); Zhiyuan Yu (Department of M
 athematics, Hong Kong University of Science and Technology); Zhize Zhou (C
 apital University of Physical Education and Sports); Lixin Fan and Haijun 
 Yang (WeBank); Can Yang (Department of Mathematics, Hong Kong University o
 f Science and Technology); and Xiaowei Zhou (State Key Laboratory of CAD&C
 G, Zhejiang Univerisity)\n---------------------\nEfficient Human Motion Re
 construction from Monocular Videos with Physical Consistency Loss\n\nVisio
 n-only motion reconstruction from monocular videos often produces artifact
 s such as foot sliding and jittery motions. Existing physics-based methods
  typically either simplify the problem to focus solely on feet-ground cont
 acts, or reconstruct full-body contacts within a physics simulator, neces.
 ..\n\n\nLin Cong and Philipp Ruppel (Universität Hamburg), Yizhou Wang (Pe
 king University), Xiang Pan (Diago Tech Company), and Norman Hendrich and 
 Jianwei Zhang (Universität Hamburg)\n---------------------\nA Locality-bas
 ed Neural Solver for Optical Motion Capture\n\nWe present a novel locality
 -based learning method for cleaning and solving optical motion capture dat
 a. Given noisy marker data, we propose a new heterogeneous graph neural ne
 twork which treats markers and joints as different types of nodes, and use
 s graph convolution operations to extract the local...\n\n\nXiaoyu Pan and
  Bowen Zheng (State Key Laboratory of CAD & CG, Zhejiang University; ZJU-T
 encent Game and Intelligent Graphics Innovation Technology Joint Lab); Xin
 wei Jiang, Guanglong Xu, Xianli Gu, and Jingxiang Li (Tencent Games Digita
 l Content Technology Center); Qilong Kou (Tencent Technology (Shenzhen) Co
 ., LTD); He Wang (University College London (UCL)); Tianjia Shao and Kun Z
 hou (State Key Laboratory of CAD & CG, Zhejiang University); and Xiaogang 
 Jin (State Key Laboratory of CAD & CG, Zhejiang University; ZJU-Tencent Ga
 me and Intelligent Graphics Innovation Technology Joint Lab)\n------------
 ---------\nAdaptive Tracking of a Single-Rigid-Body Character in Various E
 nvironments\n\nSince the introduction of DeepMimic [Peng et al. 2018], sub
 sequent research\nhas focused on expanding the repertoire of simulated mot
 ions across various\nscenarios. In this study, we propose an alternative a
 pproach for this goal,\na deep reinforcement learning method based on the 
 simulation of a single...\n\n\nTaesoo Kwon, Taehong Gu, Jaewon Ahn, and Yo
 onsang Lee (Hanyang University)\n---------------------\nFusing Monocular I
 mages and Sparse IMU Signals for Real-time Human Motion Capture\n\nEither 
 RGB images or inertial signals have been used for the task of motion captu
 re (mocap), but combining them together is a new and interesting topic. We
  believe that the combination is complementary and able to solve the inher
 ent difficulties of using one modality input, including occlusions, ext...
 \n\n\nShaohua Pan, Qi Ma, and Xinyu Yi (Tsinghua University); Weifeng Hu, 
 Xiong Wang, Xingkang ZHOU, and Jijunnan LI (OPPO Research Institute); and 
 Feng Xu (Tsinghua University)\n\nRegistration Category: Full Access\n\nSes
 sion Chair: Yuting Ye (Reality Labs Research, Meta; Meta)
END:VEVENT
END:VCALENDAR