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DTSTAMP:20250110T023301Z
LOCATION:G405\, G Block\, Level 4
DTSTART;TZID=Asia/Tokyo:20241205T160400
DTEND;TZID=Asia/Tokyo:20241205T161600
UID:siggraphasia_SIGGRAPH Asia 2024_sess276_artp_218@linklings.com
SUMMARY:ScribGen: Generating Scribble Art Through Metaheuristics
DESCRIPTION:Art Papers\n\nSoumyaratna Debnath, Ashish Tiwari, and Shanmuga
 nathan Raman (Indian Institute of Technology Gandhinagar)\n\nScribble art,
  arising from chaos and randomness, remains one of the exceptionally attra
 ctive forms of art. Many works bridge the gap between sketches and images,
  but few translate images into meaningful chaotic expressions. While deep 
 generative networks are known for understanding images, their ability to i
 nduce scribble drawings is under-explored. Unlike GAN-based approaches tha
 t generate line drawings, sketches, and contours, our work uses metaheuris
 tics to produce scribble art from images. We extensively analyse various m
 etaheuristic algorithms, demonstrating their optimal balance between creat
 ivity and computational efficiency. They offer better adaptability and acc
 uracy than state-of-the-art deep generative models for image-to-scribble g
 eneration.\n\nRegistration Category: Enhanced Access, Full Access, Full Ac
 cess Supporter\n\nLanguage Format: English Language\n\nSession Chair: Vict
 oria Szabo (Duke University, ACM)
URL:https://asia.siggraph.org/2024/program/?id=artp_218&sess=sess276
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