title: FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent
    Font Effect Generation
summary: Recently, the application of modern diffusion-based text-to-image generation
  models for creating artistic fonts, traditionally the domain of professional
  designers, has garnered significant interest. Diverging from the majority of
  existing studies that concentrate on generating artistic typography, our
  research aims to tackle a novel and more demanding challenge: the generation of
  text effects for multilingual fonts. This task essentially requires generating
  coherent and consistent visual content within the confines of a font-shaped
  canvas, as opposed to a traditional rectangular canvas. To address this task,
  we introduce a novel shape-adaptive diffusion model capable of interpreting the
  given shape and strategically planning pixel distributions within the irregular
  canvas. To achieve this, we curate a high-quality shape-adaptive image-text
  dataset and incorporate the segmentation mask as a visual condition to steer
  the image generation process within the irregular-canvas. This approach enables
  the traditionally rectangle canvas-based diffusion model to produce the desired
  concepts in accordance with the provided geometric shapes. Second, to maintain
  consistency across multiple letters, we also present a training-free,
  shape-adaptive effect transfer method for transferring textures from a
  generated reference letter to others. The key insights are building a font
  effect noise prior and propagating the font effect information in a
  concatenated latent space. The efficacy of our FontStudio system is confirmed
  through user preference studies, which show a marked preference (78% win-rates
  on aesthetics) for our system even when compared to the latest unrivaled
  commercial product, Adobe Firefly.
paper_authors._id: 666a662ecf27eed0f21606b1
paper_authors.name: Xinzhi Mu
paper_authors._id: 666a662ecf27eed0f21606b2
paper_authors.name: Li Chen
paper_authors._id: 666a662ecf27eed0f21606b3
paper_authors.name: Bohan Chen
paper_authors._id: 666a662ecf27eed0f21606b4
paper_authors.name: Shuyang Gu
paper_authors.status: claimed_verified
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paper_authors.fullname: cientgu
paper_authors.type: user
paper_authors.user: cientgu
paper_authors._id: 666a662ecf27eed0f21606b5
paper_authors.name: Jianmin Bao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-13T20:17:03.502Z
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paper_authors.avatarUrl: /avatars/e89a3c8366df504a95dc08a1a412bf3d.svg
paper_authors.fullname: Jianmin Bao
paper_authors.type: user
paper_authors.user: jianmin-ustc
paper_authors._id: 666a662ecf27eed0f21606b6
paper_authors.name: Dong Chen
paper_authors._id: 666a662ecf27eed0f21606b7
paper_authors.name: Ji Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-13T18:37:03.168Z
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paper_authors.fullname: aaa
paper_authors.type: user
paper_authors.user: jiangsuliji
paper_authors._id: 666a662ecf27eed0f21606b8
paper_authors.name: Yuhui Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-13T20:17:21.320Z
paper_authors._id: 631f108bb45367a05fe74260
paper_authors.avatarUrl: /avatars/c20da6800b643728062712d9a2771648.svg
paper_authors.fullname: Researcher
paper_authors.type: user
paper_authors.user: YuanYuhui
paper_title: FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent
    Font Effect Generation
paper_summary: Recently, the application of modern diffusion-based text-to-image generation
  models for creating artistic fonts, traditionally the domain of professional
  designers, has garnered significant interest. Diverging from the majority of
  existing studies that concentrate on generating artistic typography, our
  research aims to tackle a novel and more demanding challenge: the generation of
  text effects for multilingual fonts. This task essentially requires generating
  coherent and consistent visual content within the confines of a font-shaped
  canvas, as opposed to a traditional rectangular canvas. To address this task,
  we introduce a novel shape-adaptive diffusion model capable of interpreting the
  given shape and strategically planning pixel distributions within the irregular
  canvas. To achieve this, we curate a high-quality shape-adaptive image-text
  dataset and incorporate the segmentation mask as a visual condition to steer
  the image generation process within the irregular-canvas. This approach enables
  the traditionally rectangle canvas-based diffusion model to produce the desired
  concepts in accordance with the provided geometric shapes. Second, to maintain
  consistency across multiple letters, we also present a training-free,
  shape-adaptive effect transfer method for transferring textures from a
  generated reference letter to others. The key insights are building a font
  effect noise prior and propagating the font effect information in a
  concatenated latent space. The efficacy of our FontStudio system is confirmed
  through user preference studies, which show a marked preference (78% win-rates
  on aesthetics) for our system even when compared to the latest unrivaled
  commercial product, Adobe Firefly.
paper_ai_summary: The introduced FontStudio system uses a shape-adaptive diffusion model to generate text effects for multilingual fonts, maintaining coherence across characters and achieving superior aesthetic results compared to Adobe Firefly.
paper_ai_keywords: diffusion-based text-to-image generation shape-adaptive diffusion model irregular canvas segmentation mask image-text dataset concatenation latent space font effect noise prior shape-adaptive effect transfer user preference studies Adobe Firefly