BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:18871231T000000
DTSTART:19881009T020000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_207@linklings.com
SUMMARY:Woven Fabric Capture from a Single Photo
DESCRIPTION:Technical Papers\n\nWoven Fabric Capture from a Single Photo\n
 \nJin, Wang, Hasan, Guo, Marschner...\n\nDigitally reproducing the appeara
 nce of woven fabrics is important in many applications of realistic render
 ing, from interior scenes to virtual characters. However, designing realis
 tic shading models and capturing real fabric samples are both challenging 
 tasks. Previous work ranges from applying generic shading models not meant
  for fabrics, to data-driven approaches scanning fabrics requiring expensi
 ve setups and large data. None of these approaches can turn a single woven
  fabric sample photograph into a  high-accuracy reconstruction enabling co
 mpact storage and efficient rendering.\nIn this paper, we propose a woven 
 fabric material model and a parameter estimation approach for it. Our ligh
 tweight forward shading model treats yarns as bent and twisted cylinders, 
 shading these using a microflake-based BRDF model. We propose a simple fab
 ric capture configuration, wrapping the fabric sample on a cylinder of kno
 wn radius and capturing a single image under known camera and light positi
 ons. Our inverse rendering pipeline consists of a neural network to estima
 te initial fabric parameters and an optimization based on differentiable r
 endering to refine the results. Our fabric parameter estimation achieves h
 igh-quality recovery of measured woven fabric samples, which can be used f
 or efficient rendering and further edited.\n\nRegistration Category: FULL 
 ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLang
 uage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_207&sess=sess15
 3
END:VEVENT
END:VCALENDAR