Graph2Pix: A Graph-Based Image to Image Translation Framework
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Abstract (arXiv)
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 (http://artbreeder.com), 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 https://github.com/catlab-team/graph2pix.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 40 references that are in the map. First posted on arXiv 22 Aug 2021.
Builds on
29 of 40 references.
References · 29
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17 more
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Citing papers per year: 2021: 1.
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- 2021Exploring Latent Dimensions of Crowd-sourced Creativitycited by 0
- 2021Exploring Latent Dimensions of Crowd-sourced Creativity13 Dec 2021