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:20260114T163652Z
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:Xingjian Han, Ben Senderling, Stanley To, Deepak Kumar, and Em
 ily Whiting (Boston University) and Jun Saito (Adobe Research)\n\nThe phys
 ical plausibility of human motions is vital to various applications in the
  fields including but not limited to graphics, animation, robotics, vision
 , biomechanics, and sports science. While fully simulating human motions w
 ith physics is an extreme challenge, we hypothesize that we can treat this
  complexity as a black box in a data-driven manner if we focus on the grou
 nd contact, and have sufficient observations of physics and human activiti
 es in the real world. To prove our hypothesis, we present GroundLink, a un
 ified dataset comprised of captured ground reaction force (GRF) and center
  of pressure (CoP) synchronized to the standard kinematic motion captures.
  GRF and CoP of GroundLink are not simulated but captured at high temporal
  resolution using force platforms embedded in the ground for uncompromisin
 g measurement accuracy. This dataset contains 368 processed motion trials 
 (1.59M recorded frames) with 19 different movements including locomotion a
 nd weight-shifting actions such as tennis swings to signify the importance
  of capturing physics paired with kinematics. GroundLinkNet, our benchmark
  neural network model trained with GroundLink, supports our hypothesis by 
 predicting GRFs and CoPs accurately and plausibly on unseen motions from v
 arious sources. The dataset, code, and benchmark models will be made publi
 c for further research on various downstream tasks leveraging the rich phy
 sics information.\n\nRegistration Category: Full Access\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_947&sess=sess165
END:VEVENT
END:VCALENDAR