CONDITIONAL ADVERSARIAL IMAGE-TO-IMAGE TRANSLATION WITH U-NET GENERATOR, PATCHGAN DISCRIMINATOR, AND VGG19 PERCEPTUAL LOSS

Abstract

The demand for converting hand drawings into photorealistic images can be attributed to the difficulty of generating rich visuals from sparse, abstract, and incomplete inputs. The rapid growth of creative and design applications by the demand of automation requires better strategies for image synthesis. However, while generative modeling has been proposed as a combination of adversarial training, cycle consistency, and diffusion-based architectures; the use of deep generative systems, improving an architecture time and complexity. Hand drawings are input about a user’s coarse sketch. The global movement of having generative models for the public is producing many initiatives. While generative adversarial networks have demonstrated some promising results, there are still challenges, particularly in models trained for conditional generation. Advanced generative techniques in the computer vision domain addressed the critical challenge of preserving semantic layout and ensuring the judicious usage of perceptual losses for models in deep learning. In this model, we have implemented the key techniques which involve Generative Adversarial Networks (GANs), particularly pix2pix, perceptual loss functions, pre trained VGG-19 network and U-Net architecture. These techniques will provide robust solutions for photorealistic output and secure scene composition. Perceptual metrics are very crucial in providing critical insights into image quality and the mechanics of human-like similarity. We have used a conditional GAN-based architecture (Pix2Pix) with a U-Net generator and perceptual loss, trained on hand-drawn sketches for photorealistic image synthesis. Our results help in demonstrating the effectiveness of the method proposed by offering a scalable solution for the generation of realistic images.