Text to Image Synthesis using Generative Adversarial Networks

Abstract

Abstract: Image generation has been a significant field of research in computer vision and machine learning for several years. It involves generating new images that resemble real-world images based on a given input or set of inputs. This process has a wide range of applications, including video games, computer graphics, and image editing. With the advancements in deep learning, the development of generative models has revolutionized the field of image generation. Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have demonstrated remarkable success in generating high-quality images from input data. The focus of this paper is to propose a new technique for generating highquality images from text descriptions using Stack Generative Adversarial Networks (StackGAN). Through a sketch-refinement process, the problem is also divided into smaller manageable problems. The proposed StackGAN model comprises two stages, Stage-I and Stage-II. Stage-I GAN generates low-resolution images by sketching the primitive shape and colors of the object based on the provided textual description. Stage-II GAN generates high-resolution photo-realistic images with refined details by taking the Stage-I results and textual descriptions as inputs, along with detecting defects and adding details.