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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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DTSTART:18871231T000000
DTSTART:19881009T020000
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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_306@linklings.com
SUMMARY:Neural Brushstroke Engine: Learning a Latent Style Space of Intera
 ctive Drawing Tools
DESCRIPTION:Technical Papers\n\nNeural Brushstroke Engine: Learning a Late
 nt Style Space of Interactive Drawing Tools\n\nShugrina, Li, Fidler\n\nWe 
 propose Neural Brushstroke Engine, the first method to apply deep generati
 ve models\nto learn a distribution of interactive drawing tools. \nOur con
 ditional GAN model learns the latent \nspace of drawing styles from a smal
 l set (about 200) of unlabeled images in different media.\nOnce trained, a
  single model can texturize stroke patches drawn by the artist,\nemulatin
 g a diverse collection of brush styles in the latent space. In order to\n
 enable interactive painting on a canvas of arbitrary size, we design a pai
 nting engine able to support real-time seamless patch-based generation,\n
 while allowing artists direct control of stroke shape, color and thickness
 .\nWe show that the latent space learned by our model generalizes to unse
 en drawing and more experimental styles (e.g. beads) by embedding real sty
 les into the latent space. We explore other applications of the continuous
  latent space, such as optimizing brushes to enable painting in the style 
 of an existing artwork, automatic line drawing stylization, brush interpol
 ation, and even natural language search over a continuous space of drawing
  tools. Our prototype received positive feedback from a small group of dig
 ital artists.\n\nRegistration Category: FULL ACCESS, EXPERIENCE PLUS ACCES
 S, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-P
 ERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_306&sess=sess15
 3
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