title: CGB-DM: Content and Graphic Balance Layout Generation with
    Transformer-based Diffusion Model
summary: Layout generation is the foundation task of intelligent design, which
  requires the integration of visual aesthetics and harmonious expression of
  content delivery. However, existing methods still face challenges in generating
  precise and visually appealing layouts, including blocking, overlap, or spatial
  misalignment between layouts, which are closely related to the spatial
  structure of graphic layouts. We find that these methods overly focus on
  content information and lack constraints on layout spatial structure, resulting
  in an imbalance of learning content-aware and graphic-aware features. To tackle
  this issue, we propose Content and Graphic Balance Layout Generation with
  Transformer-based Diffusion Model (CGB-DM). Specifically, we first design a
  regulator that balances the predicted content and graphic weight, overcoming
  the tendency of paying more attention to the content on canvas. Secondly, we
  introduce a graphic constraint of saliency bounding box to further enhance the
  alignment of geometric features between layout representations and images. In
  addition, we adapt a transformer-based diffusion model as the backbone, whose
  powerful generation capability ensures the quality in layout generation.
  Extensive experimental results indicate that our method has achieved
  state-of-the-art performance in both quantitative and qualitative evaluations.
  Our model framework can also be expanded to other graphic design fields.
paper_authors._id: 669fb2f3e44924028d3be969
paper_authors.name: Yu Li
paper_authors._id: 669fb2f3e44924028d3be96a
paper_authors.name: Yifan Chen
paper_authors._id: 669fb2f3e44924028d3be96b
paper_authors.name: Gongye Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-05T13:37:54.003Z
paper_authors._id: 6553316bf151de82f6a23e1d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6553316bf151de82f6a23e1d/GTBkSj4Fa3OoyM6Muz_Sc.jpeg
paper_authors.fullname: Gongye Liu
paper_authors.type: user
paper_authors.user: liuhuohuo
paper_authors._id: 669fb2f3e44924028d3be96c
paper_authors.name: Jie Wu
paper_authors._id: 669fb2f3e44924028d3be96d
paper_authors.name: Yujiu Yang
paper_title: CGB-DM: Content and Graphic Balance Layout Generation with
    Transformer-based Diffusion Model
paper_summary: Layout generation is the foundation task of intelligent design, which
  requires the integration of visual aesthetics and harmonious expression of
  content delivery. However, existing methods still face challenges in generating
  precise and visually appealing layouts, including blocking, overlap, or spatial
  misalignment between layouts, which are closely related to the spatial
  structure of graphic layouts. We find that these methods overly focus on
  content information and lack constraints on layout spatial structure, resulting
  in an imbalance of learning content-aware and graphic-aware features. To tackle
  this issue, we propose Content and Graphic Balance Layout Generation with
  Transformer-based Diffusion Model (CGB-DM). Specifically, we first design a
  regulator that balances the predicted content and graphic weight, overcoming
  the tendency of paying more attention to the content on canvas. Secondly, we
  introduce a graphic constraint of saliency bounding box to further enhance the
  alignment of geometric features between layout representations and images. In
  addition, we adapt a transformer-based diffusion model as the backbone, whose
  powerful generation capability ensures the quality in layout generation.
  Extensive experimental results indicate that our method has achieved
  state-of-the-art performance in both quantitative and qualitative evaluations.
  Our model framework can also be expanded to other graphic design fields.
paper_ai_keywords: Transformer-based Diffusion Model regulator saliency bounding box graphic constraint CGB-DM content-aware graphic-aware layout generation layout representations