MontageGAN: Generation and Assembly of Multiple Components by GANs
This addresses a specific need in graphic design by enabling multi-layer image generation, but it is incremental as it adapts existing GAN methods to a new task.
The paper tackles the problem of generating multi-layer images, which are more valuable for graphic designers, by proposing MontageGAN, a GAN framework that uses local and global GANs to generate and assemble image layers, demonstrating its ability to produce such images and estimate layer placements.
A multi-layer image is more valuable than a single-layer image from a graphic designer's perspective. However, most of the proposed image generation methods so far focus on single-layer images. In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. Our method utilized a two-step approach consisting of local GANs and global GAN. Each local GAN learns to generate a specific image layer, and the global GAN learns the placement of each generated image layer. Through our experiments, we show the ability of our method to generate multi-layer images and estimate the placement of the generated image layers.