title: StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
summary: To leverage LLMs for visual synthesis, traditional methods convert raster
  image information into discrete grid tokens through specialized visual modules,
  while disrupting the model's ability to capture the true semantic
  representation of visual scenes. This paper posits that an alternative
  representation of images, vector graphics, can effectively surmount this
  limitation by enabling a more natural and semantically coherent segmentation of
  the image information. Thus, we introduce StrokeNUWA, a pioneering work
  exploring a better visual representation ''stroke tokens'' on vector graphics,
  which is inherently visual semantics rich, naturally compatible with LLMs, and
  highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly
  surpass traditional LLM-based and optimization-based methods across various
  metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up
  to a 94x speedup in inference over the speed of prior methods with an
  exceptional SVG code compression ratio of 6.9%.
paper_authors._id: 65b9d04ba1880eed44a82604
paper_authors.name: Zecheng Tang
paper_authors.status: admin_assigned
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paper_authors.fullname: Zecheng Tang
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paper_authors.user: ZetangForward
paper_authors._id: 65b9d04ba1880eed44a82605
paper_authors.name: Chenfei Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: Chenfei Wu
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paper_authors.user: Chenfei
paper_authors._id: 65b9d04ba1880eed44a82606
paper_authors.name: Zekai Zhang
paper_authors._id: 65b9d04ba1880eed44a82607
paper_authors.name: Mingheng Ni
paper_authors._id: 65b9d04ba1880eed44a82608
paper_authors.name: Shengming Yin
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paper_authors.name: Yu Liu
paper_authors._id: 65b9d04ba1880eed44a8260a
paper_authors.name: Zhengyuan Yang
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paper_authors.fullname: Zhengyuan Yang
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paper_authors.user: zyang39
paper_authors._id: 65b9d04ba1880eed44a8260b
paper_authors.name: Lijuan Wang
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paper_authors.avatarUrl: /avatars/a6f8d0573e678f79bc3c0b7897b818ce.svg
paper_authors.fullname: Lijuan Wang
paper_authors.type: user
paper_authors.user: Lijuan
paper_authors._id: 65b9d04ba1880eed44a8260c
paper_authors.name: Zicheng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:46:52.108Z
paper_authors._id: 6569f6cb44ce94a70187f407
paper_authors.avatarUrl: /avatars/414ecc9739ed23f9f395bd7cfa65055f.svg
paper_authors.fullname: Zicheng Liu
paper_authors.type: user
paper_authors.user: ZCLiu35
paper_authors._id: 65b9d04ba1880eed44a8260d
paper_authors.name: Juntao Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T09:47:09.323Z
paper_authors._id: 60d1d7f94672e8c13e7c2bce
paper_authors.avatarUrl: /avatars/be3a3990492ef1c5f3ff33cc043ae166.svg
paper_authors.fullname: Juntao Li
paper_authors.type: user
paper_authors.user: ljtsuda
paper_authors._id: 65b9d04ba1880eed44a8260e
paper_authors.name: Nan Duan
paper_title: StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
paper_summary: To leverage LLMs for visual synthesis, traditional methods convert raster
  image information into discrete grid tokens through specialized visual modules,
  while disrupting the model's ability to capture the true semantic
  representation of visual scenes. This paper posits that an alternative
  representation of images, vector graphics, can effectively surmount this
  limitation by enabling a more natural and semantically coherent segmentation of
  the image information. Thus, we introduce StrokeNUWA, a pioneering work
  exploring a better visual representation ''stroke tokens'' on vector graphics,
  which is inherently visual semantics rich, naturally compatible with LLMs, and
  highly compressed. Equipped with stroke tokens, StrokeNUWA can significantly
  surpass traditional LLM-based and optimization-based methods across various
  metrics in the vector graphic generation task. Besides, StrokeNUWA achieves up
  to a 94x speedup in inference over the speed of prior methods with an
  exceptional SVG code compression ratio of 6.9%.
paper_ai_keywords: LLMs (Large Language Models) visual synthesis raster image information discrete grid tokens specialized visual modules semantic representation visual scenes vector graphics stroke tokens representation engineering (RepE) StrokeNUWA semantic segmentation vector graphic generation SVG code compression