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Content and Graphic Balance Layout Generation with Transformer-based Diffusion Model

A new paper, “CGB-DM: Content and Graphic Balance Layout Generation with Transformer-based Diffusion Model”, presents a novel approach for generating high-quality layouts that harmonize with background images. Previous approaches often struggled with issues like blocking, overlap, and misalignment between layout elements, leading to less appealing and coherent layouts. The authors argue that these issues arise from an imbalance in learning content-aware and graphic-aware features, with most methods focusing excessively on content information while neglecting the importance of graphic features.

To address this, the authors propose a new model called Content and Graphic Balance Layout Generation with Transformer-based Diffusion Model (CGB-DM). This model utilizes a transformer-based diffusion model as its backbone, enabling powerful generation capabilities. To ensure the balance between content and graphic features, CGB-DM introduces two key innovations:

1. Content and Graphic Balance Weight Predictor: This module dynamically adjusts the weight assigned to content and graphic information during the generation process, preventing the model from overly prioritizing content at the expense of graphic elements. This helps to avoid issues like small-sized bounding boxes and misalignment between elements.

2. Saliency Bounding Box: To further enhance the alignment of geometric features between the layout representations and the background image, the model incorporates a saliency bounding box as an additional input. This bounding box explicitly extracts the spatial information of salient areas in the image, providing the model with more precise spatial guidance for generating layouts that better align with the image content.

The authors demonstrate the effectiveness of their model through extensive experiments on two public benchmarks: PKU and CGL. Their results show that CGB-DM consistently outperforms existing state-of-the-art methods in both quantitative and qualitative evaluations, achieving a significant improvement in graphic performance.

For example, on the PKU dataset, CGB-DM produces layouts that are more visually appealing, with fewer instances of blocking and overlap. Furthermore, on the CGL dataset, CGB-DM demonstrates a strong ability to generate layouts that effectively harmonize with the diverse content of the background images, even when dealing with complex scenes containing significant amounts of visual information.

The authors also conduct several ablation studies to analyze the contributions of different modules in their framework. These studies confirm the importance of each component in achieving the desired balance between content and graphic features.

CGB-DM represents a significant advancement in content-aware layout generation. Its ability to generate high-quality layouts that balance content and graphic features makes it a valuable tool for a wide range of applications, including poster design, document layout, and user interface design. The authors also suggest that their model framework can be readily extended to other graphic design fields, highlighting the potential for broader impact in the field of intelligent design.