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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20230103T035312Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T170000
DTEND;TZID=Asia/Seoul:20221208T183000
UID:siggraphasia_SIGGRAPH Asia 2022_sess167_papers_207@linklings.com
SUMMARY:Woven Fabric Capture from a Single Photo
DESCRIPTION:Technical Communications, Technical Papers\n\nWoven Fabric Cap
 ture from a Single Photo\n\nJin, Wang, Hasan, Guo, Marschner...\n\nDigital
 ly reproducing the appearance of woven fabrics is important in many applic
 ations of realistic rendering, from interior scenes to virtual characters.
  However, designing realistic shading models and capturing real fabric sam
 ples are both challenging tasks. Previous work ranges from applying generi
 c shading models not meant for fabrics, to data-driven approaches scanning
  fabrics requiring expensive setups and large data. None of these approach
 es can turn a single woven fabric sample photograph into a  high-accuracy 
 reconstruction enabling compact storage and efficient rendering.\nIn this 
 paper, we propose a woven fabric material model and a parameter estimation
  approach for it. Our lightweight forward shading model treats yarns as be
 nt and twisted cylinders, shading these using a microflake-based BRDF mode
 l. We propose a simple fabric capture configuration, wrapping the fabric s
 ample on a cylinder of known radius and capturing a single image under kno
 wn camera and light positions. Our inverse rendering pipeline consists of 
 a neural network to estimate initial fabric parameters and an optimization
  based on differentiable rendering to refine the results. Our fabric param
 eter estimation achieves high-quality recovery of measured woven fabric sa
 mples, which can be used for efficient rendering and further edited.\n\nRe
 gistration Category: FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\
 nFormat: IN-PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_207&sess=sess16
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