GAN-Based Domain Adaptation for Image-Aware Layout Generation in Advertising Poster Design
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new dataset and GAN-based models for generating graphic layouts for advertising posters. The PDA-GAN model effectively creates image-aware layouts by bridging the domain gap between product images and poster designs.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Graphic Design
Background
- Deep learning models are increasingly used for graphic layout generation.
- Generating realistic advertising poster layouts requires paired product images and layouts.
- Existing methods face challenges due to domain gaps between real images and generated content.
Purpose Of The Study
- To develop advanced Generative Adversarial Network (GAN)-based models for image-aware graphic layout generation.
- To introduce the Content-aware Graphic Layout Dataset (CGL-Dataset) for training and evaluating layout generation models.
- To propose novel content-aware metrics for assessing layout quality.
Main Methods
- Introduction of the Content-aware Graphic Layout Dataset (CGL-Dataset) with paired product images and inpainted posters.
- Development of two GAN-based models: CGL-GAN (using Gaussian blur) and PDA-GAN (incorporating unsupervised domain adaptation with a pixel-level discriminator).
- Proposal of three novel content-aware metrics for evaluating layout generation.
Main Results
- PDA-GAN effectively bridges the domain gap between inpainted posters and clean product images.
- The pixel-level discriminator in PDA-GAN enhances image-aware layout generation.
- Quantitative and qualitative evaluations show state-of-the-art performance for PDA-GAN.
Conclusions
- The proposed PDA-GAN model achieves superior performance in generating high-quality, image-aware graphic layouts for advertising posters.
- The CGL-Dataset and novel metrics facilitate further research in conditional graphic layout generation.
- Deep learning, particularly GANs with domain adaptation, shows significant potential for automated graphic design.
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