  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.
