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DTSTAMP:20260817T171530Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241203T145600
DTEND;TZID=Asia/Tokyo:20241203T150800
UID:siggraphasia_SIGGRAPH Asia 2024_sess108_papers_214@linklings.com
SUMMARY:SD-𝜋XL: Generating Low-Resolution Quantized Imagery via Score Dist
 illation
DESCRIPTION:Alexandre Binninger and Olga Sorkine-Hornung (ETH Zürich)\n\nL
 ow-resolution quantized imagery, such as pixel art, is seeing a revival in
  modern applications ranging from video game graphics to digital design an
 d fabrication, where creativity is often bound by a limited palette of ele
 mental units. Despite their growing popularity, the automated generation o
 f quantized images from raw inputs remains a significant challenge, often 
 necessitating intensive manual input. We introduce SD-𝜋XL, an approach for
  producing quantized images that employs score distillation sampling in co
 njunction with a differentiable image generator. Our method enables users 
 to input a prompt and optionally an image for spatial conditioning, set an
 y desired output size 𝐻×𝑊 , and choose a palette of 𝑛 colors or elements. 
 Each color corresponds to a distinct class for our generator, which operat
 es on an 𝐻×𝑊×𝑛 tensor. We adopt a softmax approach, computing a convex sum
  of elements, thus rendering the process differentiable and amenable to ba
 ckpropagation. We show that employing Gumbel-softmax reparameterization al
 lows for crisp pixel art effects. Unique to our method is the ability to t
 ransform input images into low-resolution, quantized versions while retain
 ing their key semantic features. Our experiments validate SD-𝜋XL’s perform
 ance in creating visually pleasing and faithful representations, consisten
 tly outperforming the current state-of-the-art. Furthermore, we showcase S
 D-𝜋XL’s practical utility in fabrication through its applications in inter
 locking brick mosaic, beading and embroidery design.\n\nRegistration Categ
 ory: Full Access, Full Access Supporter\n\nLanguage Format: English Langua
 ge\n\nSession Chair: I-Chao Shen (The University of Tokyo)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_214&sess=sess108
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