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DTSTART:18871231T000000
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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_357@linklings.com
SUMMARY:Motion Guided Deep Dynamic 3D Garments
DESCRIPTION:Technical Papers\n\nMotion Guided Deep Dynamic 3D Garments\n\n
 Zhang, Ceylan, Mitra\n\nRealistic dynamic garments on animated characters 
 have many AR/VR applications. While authoring such dynamic  garment geomet
 ry is still a challenging task, data-driven simulation provides an attract
 ive alternative, especially if it can be controlled simply using the motio
 n of the underlying character. In this work, we focus on motion guided dyn
 amic 3D garments, especially for loose garments. In a data-driven setup, w
 e first learn a generative space of plausible garment geometries. Then, we
   learn a mapping to this space  to capture the motion dependent dynamic d
 eformations, conditioned on the previous state of the garment as well as i
 ts relative position with respect to the underlying body. Technically, we 
 model garment dynamics, driven using the input character motion, by predic
 ting per-frame local displacements in a canonical state of the garment tha
 t is enriched  with frame-dependent skinning weights to bring the garment 
 to the global space. We resolve any remaining per-frame collisions by pred
 icting residual local displacements. The resultant garment geometry is use
 d as history to enable iterative roll-out prediction. We demonstrate plaus
 ible generalization to unseen body shapes and motion inputs, and show impr
 ovements over multiple state-of-the-art alternatives.\n\nRegistration Cate
 gory: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBI
 TOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_357&sess=sess15
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