Text to Image Synthesis Using Bridge Generative Adversarial Network and Char CNN Model

Abstract

A content to picture production approach seeks to produce photorealistic images that are semantically coherent with the provided descriptions from text descriptions. Applications for creating photorealistic visuals from text includes photo editing and more. Strong neural network topologies, such as GANs (Generative Adversarial Networks) have been shown to produce effective outcomes in recent years. Two very significant factors, visual reality and content consistency, must be taken into consideration when creating images from text descriptions. Recent substantial advancements in GAN have made it possible to produce images with a high level of visual realism. However, generating images from text ensuring high content consistency between the text and the generated image is still ambitious. To address the above two issues, a Bridge GAN model is proposed, where the bridge is a transitional space containing meaningful representations of the given text description. The proposed systems incorporate Bridge GAN and char CNN – RNN model to generate the image in high content consistency and the results shows that the proposed system outperformed the existing systems.