Generative AI for Fashion Trends Suggestions

Generative AI for Fashion Trends Suggestions

Author Name : Dr. K Lakshmi, Arifa A, Swetha Ambroise G, Hema Rehkas

ABSTRACT This paper explores the transformative potential of Generative Adversarial Networks (GANs) in the fashion retail industry, focusing on diversifying and personalizing fashion designs. By leveraging GANs, the industry can efficiently generate novel product designs at a low cost, addressing the crucial balance between aesthetics and consumer satisfaction. Increasing the representation of all body typesthrough generated models contributes to a more realistic and accurate portrayal of fashion products. Drawing inspiration from the success of Dove's diverse advertising campaign, which led to a 600% increase in sales, the thesis underscores the market impact of inclusivity. The problem statement introduces the innovative project, "Generative AI for Fashion Trends Suggestions and Analysis," which aims to revolutionize style discovery through a GUI application. This application employs a steady diffusion algorithm, blending full-sized portraits with suggested outfits, creating visually striking and customized fashion ensembles based on user preferences. The project's use of a diverse dataset enables the AI model to generate original and appropriate outfit suggestions, showcasing the synergy of art and technology in offering a unique and personalized consumer experience.