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BEGIN:VEVENT
DTSTAMP:20240214T070246Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T093000
DTEND;TZID=Australia/Melbourne:20231214T094000
UID:siggraphasia_SIGGRAPH Asia 2023_sess165_papers_947@linklings.com
SUMMARY:GroundLink: A Dataset Unifying Human Body Movement and Ground Reac
 tion Dynamics
DESCRIPTION:Technical Papers, TOG\n\nXingjian Han, Ben Senderling, Stanley
  To, Deepak Kumar, and Emily Whiting (Boston University) and Jun Saito (Ad
 obe Research)\n\nThe physical plausibility of human motions is vital to va
 rious applications in the fields including but not limited to graphics, an
 imation, robotics, vision, biomechanics, and sports science. While fully s
 imulating human motions with physics is an extreme challenge, we hypothesi
 ze that we can treat this complexity as a black box in a data-driven manne
 r if we focus on the ground contact, and have sufficient observations of p
 hysics and human activities in the real world. To prove our hypothesis, we
  present GroundLink, a unified dataset comprised of captured ground reacti
 on force (GRF) and center of pressure (CoP) synchronized to the standard k
 inematic motion captures. GRF and CoP of GroundLink are not simulated but 
 captured at high temporal resolution using force platforms embedded in the
  ground for uncompromising measurement accuracy. This dataset contains 368
  processed motion trials (1.59M recorded frames) with 19 different movemen
 ts including locomotion and weight-shifting actions such as tennis swings 
 to signify the importance of capturing physics paired with kinematics. Gro
 undLinkNet, our benchmark neural network model trained with GroundLink, su
 pports our hypothesis by predicting GRFs and CoPs accurately and plausibly
  on unseen motions from various sources. The dataset, code, and benchmark 
 models will be made public for further research on various downstream task
 s leveraging the rich physics information.\n\nRegistration Category: Full 
 Access
URL:https://asia.siggraph.org/2023/full-program?id=papers_947&sess=sess165
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