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Graph2Pix: A Graph-Based Image to Image Translation Framework

2021-08-22 15:20:06
Dilara Gokay, Enis Simsar, Efehan Atici, Alper Ahmetoglu, Atif Emre Yuksel, Pinar Yanardag

Abstract

In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder (this http URL), where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n=60), where users preferred the images generated by our method 81.5% of the time. Our source code and dataset are publicly available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2108.09752

PDF

https://arxiv.org/pdf/2108.09752.pdf


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