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DTSTAMP:20260817T171543Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241205T104500
DTEND;TZID=Asia/Tokyo:20241205T115500
UID:siggraphasia_SIGGRAPH Asia 2024_sess129@linklings.com
SUMMARY:Capture Me If You Can
DESCRIPTION:Each Paper gives a 10 minute presentation.\n\nMillimetric Huma
 n Surface Capture in Minutes\n\nDetailed human surface capture from multip
 le images is an essential component for many 3D production, analysis and t
 ransmission tasks. Yet producing millimetric precision 3D models in practi
 cal time, and actually verifying their 3D accuracy in a real-world capture
  context, remain key challenges due ...\n\n\nBriac Toussaint and Laurence 
 Boissieux (Centre Inria de l’Université Grenoble Alpes); Diego Thomas (Kyu
 shu University); Edmond Boyer (Meta Reality Labs Research); and Jean-Sébas
 tien Franco (LJK, CNRS, Grenoble INP, Université Grenoble Alpes; Centre In
 ria de l’Université Grenoble Alpes)\n---------------------\nRoMo: A Robust
  Solver for Full-body Unlabeled Optical Motion Capture\n\nOptical motion c
 apture (MoCap) is the "gold standard" for accurately capturing full-body m
 otions. To make use of raw MoCap point data, the system labels the points 
 with corresponding body part locations and solves the full-body motions. H
 owever, MoCap data often contains mislabeling, occlusion and p...\n\n\nXia
 oyu Pan and Bowen Zheng (State Key Laboratory of CAD&CG, Zhejiang Universi
 ty); Xinwei Jiang, Zijiao Zeng, and Qilong Kou (Tencent Games Digital Cont
 ent Technology Center); He Wang (Department of Computer Science and UCL Ce
 ntre for Artificial Intelligence, University College London); and Xiaogang
  Jin (State Key Laboratory of CAD&CG, Zhejiang University)\n--------------
 -------\nFürElise: Capturing and Physically Synthesizing Hand Motion of Pi
 ano Performance\n\nPiano playing requires agile, precise, and coordinated 
 hand control that stretches the limits of dexterity. Hand motion models wi
 th the sophistication to accurately recreate piano playing have a wide ran
 ge of applications in character animation, embodied AI, biomechanics, and 
 VR/AR. In this paper, w...\n\n\nRuocheng Wang, Pei Xu, Haochen Shi, Elizab
 eth Schumann, and C. Karen Liu (Stanford University)\n--------------------
 -\nLook Ma, no markers: holistic performance capture without the hassle\n\
 nWe tackle the problem of highly-accurate, holistic performance capture fo
 r the face, body and hands simultaneously. Motion-capture technologies use
 d in film and game production typically focus only on face, body or hand c
 apture independently, involve complex and expensive hardware and a high de
 gree ...\n\n\nCharlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Pet
 ikam, Shideh Rezaeifar, Louis Florentin, Zafiirah Hosenie, Thomas J. Cashm
 an, and Julien Valentin (Microsoft); Darren Cosker (Microsoft, University 
 of Bath); and Tadas Baltrusaitis (Microsoft)\n---------------------\nEgoHD
 M: An Online Egocentric-Inertial Human Motion Capture, Localization, and D
 ense Mapping System\n\nWe present EgoHDM, an online egocentric-inertial hu
 man motion capture (mocap), localization, and dense mapping system. Our sy
 stem uses 6 inertial measurement units (IMUs) and a commodity head-mounted
  RGB camera. EgoHDM is the first human mocap system that offers dense scen
 e mapping in near real-time...\n\n\nHandi Yin and Bonan Liu (Hong Kong Uni
 versity of Science and Technology, Guangzhou); Manuel Kaufmann (ETH Zürich
 ); Jinhao He (Hong Kong University of Science and Technology, Guangzhou); 
 Sammy Christen (ETH Zürich); and Jie Song and Pan Hui (Hong Kong Universit
 y of Science and Technology, Guangzhou; Hong Kong University of Science an
 d Technology)\n---------------------\nELMO: Enhanced Real-time LiDAR Motio
 n Capture through Upsampling\n\nThis paper introduces ELMO, a real-time up
 sampling motion capture framework designed for a single LiDAR sensor. Mode
 led as a conditional autoregressive transformer-based upsampling motion ge
 nerator, ELMO achieves 60 fps motion capture from a 20 fps LiDAR point clo
 ud sequence. The key feature of ELMO...\n\n\nDeok-Kyeong Jang (MOVIN Inc.)
 ; Dongseok Yang (MOVIN Inc., KAIST); Deok-Yun Jang (MOVIN Inc., GIST); Bye
 oli Choi (MOVIN Inc., KAIST); Donghoon Shin (MOVIN Inc.); and Sung-Hee Lee
  (KAIST)\n\nRegistration Category: Full Access, Full Access Supporter\n\nL
 anguage Format: English Language\n\nSession Chair: Yuting Ye (Reality Labs
  Research, Meta; Meta)
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