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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20230103T035306Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_239@linklings.com
SUMMARY:Dressing Avatars: Deep Photorealistic Appearance for Physically Si
 mulated Clothing
DESCRIPTION:Technical Papers\n\nDressing Avatars: Deep Photorealistic Appe
 arance for Physically Simulated Clothing\n\nXiang, Bagautdinov, Stuyck, Pr
 ada, Romero...\n\nDespite recent progress in developing animatable full-bo
 dy avatars, realistic modeling of clothing - one of the core aspects of hu
 man self-expression - remains an open challenge. State-of-the-art physical
  simulation methods can generate realistically behaving clothing geometry 
 at interactive rates. Modeling photorealistic appearance, however, usually
  requires physically-based rendering which is too expensive for interactiv
 e applications. On the other hand, data-driven deep appearance models are 
 capable of efficiently producing realistic appearance, but struggle at syn
 thesizing geometry of highly dynamic clothing and handling challenging bod
 y-clothing configurations. To this end, we introduce pose-driven avatars w
 ith explicit modeling of clothing that exhibit both photorealistic appeara
 nce learned from real-world data and realistic clothing dynamics. The key 
 idea is to introduce a neural clothing appearance model that operates on t
 op of explicit geometry: at training time we use high-fidelity tracking, w
 hereas at animation time we rely on physically simulated geometry. Our cor
 e contribution is a physically-inspired appearance network, capable of gen
 erating photorealistic appearance with view-dependent and dynamic shadowin
 g effects even for unseen body-clothing configurations. We conduct a thoro
 ugh evaluation of our model and demonstrate diverse animation results on s
 everal subjects and different types of clothing. Unlike previous work on p
 hotorealistic full-body avatars, our approach can produce much richer dyna
 mics and more realistic deformations even for many examples of loose cloth
 ing. We also demonstrate that our formulation naturally allows clothing to
  be used with avatars of different people while staying fully animatable, 
 thus enabling, for the first time, photorealistic avatars with novel cloth
 ing.\n\nRegistration Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERI
 ENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_239&sess=sess15
 3
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