CGB-DM: Content and Graphic Balance Layout Generation with
  Transformer-based Diffusion Model
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.
669fb2f3e44924028d3be969
Yu Li
669fb2f3e44924028d3be96a
Yifan Chen
669fb2f3e44924028d3be96b
Gongye Liu
claimed_verified
2025-02-05T13:37:54.003Z
6553316bf151de82f6a23e1d
https://cdn-avatars.huggingface.co/v1/production/uploads/6553316bf151de82f6a23e1d/GTBkSj4Fa3OoyM6Muz_Sc.jpeg
Gongye Liu
user
liuhuohuo
669fb2f3e44924028d3be96c
Jie Wu
669fb2f3e44924028d3be96d
Yujiu Yang
CGB-DM: Content and Graphic Balance Layout Generation with
  Transformer-based Diffusion Model
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.
A transformer-based diffusion model with content and graphic balance and saliency bounding box constraints achieves state-of-the-art layout generation quality and applicability to other design fields.
transformer-based diffusion model saliency bounding box content-aware graphic-aware layout representation layout generation