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DTSTART:18871231T000000
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DTSTAMP:20230103T035312Z
LOCATION:Room 325-AB\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221209T103000
DTEND;TZID=Asia/Seoul:20221209T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess177_papers_561@linklings.com
SUMMARY:Capturing and Animation of Body and Clothing from Monocular Video
DESCRIPTION:Technical Papers\n\nCapturing and Animation of Body and Clothi
 ng from Monocular Video\n\nFeng, Yang, Pollefeys, Black, Bolkart\n\nWhile 
 recent work has shown progress on extracting clothed 3D human avatars from
  a single image, video, or a set of 3D scans, several limitations remain. 
 Most methods use a holistic representation to jointly model the body and c
 lothing, which means that the clothing and body cannot be separated for ap
 plications like virtual try-on. Other methods separately model the body an
 d clothing, but they require training from a large set of 3D clothed human
  meshes obtained from 3D/4D scanners or physics simulations. Our insight i
 s that the body and clothing have different modeling requirements. While t
 he body is well represented by a mesh-based parametric 3D model, implicit 
 representations and neural radiance fields are better suited to capturing 
 the large variety in shape and appearance present in clothing. Building on
  this insight, we propose SCARF (Segmented Clothed Avatar Radiance Field),
  a hybrid model combining a mesh-based body with a neural radiance field. 
 Integrating the mesh into the volumetric rendering in combination with a d
 ifferentiable rasterizer enables us to optimize SCARF directly from monocu
 lar videos, without any 3D supervision. The hybrid modeling enables SCARF 
 to (i) animate the clothed body avatar by changing body poses (including h
 and articulation and facial expressions), (ii) synthesize novel views of t
 he avatar, and (iii) transfer clothing between avatars in virtual try-on a
 pplications. We demonstrate that SCARF reconstructs clothing with higher v
 isual quality than existing methods, that the clothing deforms with changi
 ng body pose and body shape, and that clothing can be successfully transfe
 rred between avatars of different subjects.\n\nRegistration Category: FULL
  ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DE
 MAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_561&sess=sess17
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