Adversarial Text to Continuous Image Generation
Kilichbek Haydarov, Aashiq Muhamed, Xiaoqian Shen, Jovana Lazarevic, Ivan Skorokhodov, Chamuditha Jayanga Galappaththige, Mohamed Elhoseiny
Abstract
Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator that controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retains the properties of continuous generative models.
BibTeX
@inproceedings{cvpr2024_adversarialtextt,
title = {Adversarial Text to Continuous Image Generation},
author = {Kilichbek Haydarov and Aashiq Muhamed and Xiaoqian Shen and Jovana Lazarevic and Ivan Skorokhodov and Chamuditha Jayanga Galappaththige and Mohamed Elhoseiny},
booktitle = {CVPR 2024},
year = {2024}
}