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:20260114T163645Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T181300
DTEND;TZID=Australia/Melbourne:20231213T182300
UID:siggraphasia_SIGGRAPH Asia 2023_sess167_papers_443@linklings.com
SUMMARY:Discovering Fatigued Movements for Virtual Character Animation
DESCRIPTION:Noshaba Cheema (German Research Center for Artificial Intellig
 ence, Max-Planck Institute for Informatics); Rui Xu (German Research Cente
 r for Artificial Intelligence, Saarland University); Nam Hee Kim and Pertt
 u Hämäläinen (Aalto University); Vladislav Golyanik and Marc Habermann (Ma
 x-Planck-Institut für Informatik); Christian Theobalt (Max-Planck-Institut
  für Informatik, Saarland University); and Philipp Slusallek (Saarland Uni
 versity, German Research Center for Artificial Intelligence)\n\nVirtual ch
 aracter animation and movement synthesis have advanced rapidly during rece
 nt years, especially through a combination of extensive motion capture dat
 asets and machine learning. A remaining challenge is interactively simulat
 ing characters that fatigue when performing extended motions, which is ind
 ispensable for the realism of generated animations. However, capturing suc
 h movements is problematic, as performing movements like backflips with fa
 tigued variations up to exhaustion raises capture cost and risk of injury.
  Surprisingly, little research has been done on faithful fatigue modeling.
  To address this, we propose a deep reinforcement learning-based approach,
  which---for the first time in literature---generates control policies for
  full-body physically simulated agents aware of cumulative fatigue. For th
 is, we first leverage Generative Adversarial Imitation Learning (GAIL) to 
 learn an expert policy for the skill; Second, we learn a fatigue policy by
  limiting the generated constant torque bounds based on endurance time to 
 non-linear, state- and time-dependent limits in the joint-actuation space 
 using a Three-Compartment Controller (3CC) model. Our results demonstrate 
 that agents can adapt to different fatigue and rest rates interactively, a
 nd discover realistic recovery strategies without the need for any capture
 d data of fatigued movement.\n\nRegistration Category: Full Access\n\nSess
 ion Chair: Yoonsang Lee (Hanyang University)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_443&sess=sess167
END:VEVENT
END:VCALENDAR