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DTSTAMP:20240214T070241Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_947@linklings.com
SUMMARY:GroundLink: A Dataset Unifying Human Body Movement and Ground Reac
 tion Dynamics
DESCRIPTION:Technical Papers\n\nXingjian Han, Ben Senderling, Stanley To, 
 Deepak Kumar, and Emily Whiting (Boston University) and Jun Saito (Adobe R
 esearch)\n\nThe physical plausibility of human motions is vital to various
  applications in the fields including but not limited to graphics, animati
 on, robotics, vision, biomechanics, and sports science. While fully simula
 ting human motions with physics is an extreme challenge, we hypothesize th
 at we can treat this complexity as a black box in a data-driven manner if 
 we focus on the ground contact, and have sufficient observations of physic
 s and human activities in the real world. To prove our hypothesis, we pres
 ent GroundLink, a unified dataset comprised of captured ground reaction fo
 rce (GRF) and center of pressure (CoP) synchronized to the standard kinema
 tic motion captures. GRF and CoP of GroundLink are not simulated but captu
 red at high temporal resolution using force platforms embedded in the grou
 nd for uncompromising measurement accuracy. This dataset contains 368 proc
 essed motion trials (1.59M recorded frames) with 19 different movements in
 cluding locomotion and weight-shifting actions such as tennis swings to si
 gnify the importance of capturing physics paired with kinematics. GroundLi
 nkNet, our benchmark neural network model trained with GroundLink, support
 s our hypothesis by predicting GRFs and CoPs accurately and plausibly on u
 nseen motions from various sources. The dataset, code, and benchmark model
 s will be made public for further research on various downstream tasks lev
 eraging the rich physics information.\n\nRegistration Category: Full Acces
 s, Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor
URL:https://asia.siggraph.org/2023/full-program?id=papers_947&sess=sess209
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