GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design

• URL: http://arxiv.org/abs/2604.07409v1
• Date: Wed, 08 Apr 2026 11:52:41 GMT
• Title: GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design
• Abstract summary: This paper focuses on using a GAN-based model conditioned on images to generate advertising poster graphic layouts.<n>We propose three novel content-aware metrics to assess the model's ability to capture the intricate relationships between graphic elements and image content.
• Score: 31.116845792750052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Layout plays a crucial role in graphic design and poster generation. Recently, the application of deep learning models for layout generation has gained significant attention. This paper focuses on using a GAN-based model conditioned on images to generate advertising poster graphic layouts, requiring a dataset of paired product images and layouts. To address this task, we introduce the Content-aware Graphic Layout Dataset (CGL-Dataset), consisting of 60,548 paired inpainted posters with annotations and 121,000 clean product images. The inpainting artifacts introduce a domain gap between the inpainted posters and clean images. To bridge this gap, we design two GAN-based models. The first model, CGL-GAN, uses Gaussian blur on the inpainted regions to generate layouts. The second model combines unsupervised domain adaptation by introducing a GAN with a pixel-level discriminator (PD), abbreviated as PDA-GAN, to generate image-aware layouts based on the visual texture of input images. The PD is connected to shallow-level feature maps and computes the GAN loss for each input-image pixel. Additionally, we propose three novel content-aware metrics to assess the model's ability to capture the intricate relationships between graphic elements and image content. Quantitative and qualitative evaluations demonstrate that PDA-GAN achieves state-of-the-art performance and generates high-quality image-aware layouts.

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