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:20240214T070248Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T142000
DTEND;TZID=Australia/Melbourne:20231214T143500
UID:siggraphasia_SIGGRAPH Asia 2023_sess151_papers_544@linklings.com
SUMMARY:ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Lay
 ered Clothes
DESCRIPTION:Technical Papers\n\nDohae Lee, Hyun Kang, and In-Kwon Lee (Yon
 sei University)\n\nWe present ClothCombo, a pipeline to drape arbitrary co
 mbinations of clothes on 3D human models with varying body shapes and pose
 s. While existing learning-based approaches for draping clothes have shown
  promising results, multi-layered clothing remains challenging as it is no
 n-trivial to model inter-cloth interaction. To this end, our method utiliz
 es a GNN-based network to efficiently model the interaction between clothe
 s in different layers, thus enabling multi-layered clothing. Specifically,
  we first create feature embedding for each cloth using a topology-agnosti
 c network. Then, the draping network deforms all clothes to fit the target
  body shape and pose without considering inter-cloth interaction. Lastly, 
 the untangling network predicts the per-vertex displacements in a way that
  resolves interpenetration between clothes. \nIn experiments, the proposed
  model demonstrates strong performance in complex multi-layered scenarios.
  Being agnostic to cloth topology, our method can be readily used for laye
 red virtual try-on of real clothes in diverse poses and combinations of cl
 othes.\n\nRegistration Category: Full Access\n\nSession Chair: Tao Du (Tsi
 nghua University, Shanghai Qi Zhi Institute)
URL:https://asia.siggraph.org/2023/full-program?id=papers_544&sess=sess151
END:VEVENT
END:VCALENDAR