Prompt2Fashion: An automatically generated fashion dataset

TL;DR

Prompt2Fashion introduces an automatically generated, personalized fashion dataset created using generative models and large language models, addressing the lack of comprehensive datasets for AI-driven fashion design and customization.

Contribution

This work presents a novel method for automatically constructing a diverse, high-quality fashion dataset tailored to individual needs using generative models and prompting strategies.

Findings

Generated outfits are of high aesthetic quality and relevance.

The dataset is publicly available for research use.

Evaluation highlights the importance of expert assessment for artistic datasets.

Abstract

Despite the rapid evolution and increasing efficacy of language and vision generative models, there remains a lack of comprehensive datasets that bridge the gap between personalized fashion needs and AI-driven design, limiting the potential for truly inclusive and customized fashion solutions. In this work, we leverage generative models to automatically construct a fashion image dataset tailored to various occasions, styles, and body types as instructed by users. We use different Large Language Models (LLMs) and prompting strategies to offer personalized outfits of high aesthetic quality, detail, and relevance to both expert and non-expert users' requirements, as demonstrated by qualitative analysis. Up until now the evaluation of the generated outfits has been conducted by non-expert human subjects. Despite the provided fine-grained insights on the quality and relevance of generation, we extend the discussion on the importance of expert knowledge for the evaluation of artistic AI-generated datasets such as this one. Our dataset is publicly available on GitHub at https://github.com/georgiarg/Prompt2Fashion.