  Fashion analysis refers to the process of examining and evaluating trends,
styles, and elements within the fashion industry to understand and interpret
its current state, generating fashion reports. It is traditionally performed by
fashion professionals based on their expertise and experience, which requires
high labour cost and may also produce biased results for relying heavily on a
small group of people. In this paper, to tackle the Fashion Report Generation
(FashionReGen) task, we propose an intelligent Fashion Analyzing and Reporting
system based the advanced Large Language Models (LLMs), debbed as GPT-FAR.
Specifically, it tries to deliver FashionReGen based on effective catwalk
analysis, which is equipped with several key procedures, namely, catwalk
understanding, collective organization and analysis, and report generation. By
posing and exploring such an open-ended, complex and domain-specific task of
FashionReGen, it is able to test the general capability of LLMs in fashion
domain. It also inspires the explorations of more high-level tasks with
industrial significance in other domains. Video illustration and more materials
of GPT-FAR can be found in https://github.com/CompFashion/FashionReGen.
