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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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TZOFFSETTO:+0900
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035312Z
LOCATION:Room 325-AB\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221209T090000
DTEND;TZID=Asia/Seoul:20221209T103000
UID:siggraphasia_SIGGRAPH Asia 2022_sess176_papers_522@linklings.com
SUMMARY:Efficient Neural Style Transfer for Volumetric Simulations
DESCRIPTION:Technical Communications, Technical Papers, TOG\n\nEfficient N
 eural Style Transfer for Volumetric Simulations\n\nAurand, Ortiz, Nauer, C
 . Azevedo\n\nArtistically controlling fluids has always been a challenging
  task. Recently, volumetric Neural Style Transfer (NST) techniques have be
 en used to artistically manipulate smoke simulation data with 2D images. I
 n this work, we revisit previous volumetric NST techniques for smoke, prop
 osing a suite of upgrades that enable stylizations that are significantly 
 faster, simpler, more controllable and less prone to artifacts. \nMoreover
 ,  the energy minimization solved by previous methods is camera dependent.
  To avoid that, a computationally expensive iterative optimization perform
 ed for multiple views sampled around the original simulation is needed, wh
 ich can take up to several minutes per frame. We propose a simple feed-for
 ward neural network architecture that is able to infer view-independent st
 ylizations that are three orders of the magnitude faster than its optimiza
 tion-based counterpart.\n\nRegistration Category: FULL ACCESS, ON-DEMAND A
 CCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_522&sess=sess17
 6
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