MontageGAN: Generation and Assembly of Multiple Components by GANs

• URL: http://arxiv.org/abs/2205.15577v1
• Date: Tue, 31 May 2022 07:34:19 GMT
• Title: MontageGAN: Generation and Assembly of Multiple Components by GANs
• Authors: Chean Fei Shee, Seiichi Uchida
• Abstract summary: We propose MontageGAN, which is a Generative Adversarial Networks framework for generating multi-layer images. Our method utilized a two-step approach consisting of local GANs and global GAN.
• Score: 11.117357750374035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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