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DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
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UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_436@linklings.com
SUMMARY:Reference Based Sketch Extraction via Attention Mechanism
DESCRIPTION:Technical Papers\n\nReference Based Sketch Extraction via Atte
 ntion Mechanism\n\nAshtari, Seo, Kang, Cha, Noh\n\nWe propose a model that
  extracts a sketch from a colorized image in such a way that the extracted
  sketch has a line style similar to a given reference sketch while preserv
 ing the visual content identically to the colorized image. Authentic sketc
 hes drawn by artists have various sketch styles to add visual interest and
  contribute feeling to the sketch. However, existing sketch-extraction met
 hods generate sketches with only one style. Moreover, existing style trans
 fer models fail to transfer sketch styles because they are mostly designed
  to transfer textures of a source style image instead of transferring the 
 sparse line styles from a reference sketch. Lacking the necessary volumes 
 of data for standard training of translation systems, at the core of our G
 AN-based solution is a self-reference sketch style generator that produces
  various reference sketches with a similar style but different spatial lay
 outs. We use independent attention modules to detect the edges of a colori
 zed image and reference sketch as well as the visual correspondences betwe
 en them. We apply several loss terms to imitate the style and enforce spar
 sity in the extracted sketches. Our sketch-extraction method results in a 
 close imitation of a reference sketch style drawn by an artist and outperf
 orms all baseline methods. Using our method, we produce a synthetic datase
 t representing various sketch styles and improve the performance of auto-c
 olorization models, in high demand in comics. The validity of our approach
  is confirmed via qualitative and quantitative evaluations.\n\nRegistratio
 n Category: FULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE 
 EXHIBITOR\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_436&sess=sess15
 3
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