Dynamic Neural Style Transfer for Artistic Image Generation using VGG19

Abstract--Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of images, applying texture synthesis techniques. Nevertheless, a number of current techniques continue to encounter obstacles, including lengthy processing times, restricted choices of style images, and the inability to modify the weight ratio of styles. Recent advancements in neural style transfer have led to I. INTRODUCTION innovative techniques that enable adaptive, multi-style, and content-aware transformations, with a focus on enhancing aesthetic Neural style transfer has transformed digital art by allowing quality, computational efficiency, and user interactivity. Originally, style transfer models focused on applying for neural style transfer using a dual-generator architecture.