Graph2Pix: A Graph-Based Image To Image Translation Framework
| Autor: | Enis Simsar, Pinar Yanardag, Dilara Gokay, Efehan Atici, Atif Emre Yuksel, Alper Ahmetoglu |
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| Rok vydání: | 2021 |
| Předmět: |
Structure (mathematical logic)
FOS: Computer and information sciences Information retrieval Source code business.industry Computer science media_common.quotation_subject Computer Vision and Pattern Recognition (cs.CV) Graph based Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Translation (geometry) Image (mathematics) Benchmark (computing) Graph (abstract data type) Image translation Artificial intelligence business media_common |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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