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.

翻译：时尚分析是指通过审视与评估时尚产业中的趋势、风格及元素，以理解并阐释其当前状态并生成时尚报告的过程。传统上，这项工作由时尚专业人员凭借其专业知识和经验完成，不仅人力成本高昂，且因高度依赖少数群体而易产生主观偏差。本文针对时尚报告生成（FashionReGen）任务，提出一种基于先进大语言模型（LLMs）的智能时尚分析与报告系统，命名为GPT-FAR。具体而言，该系统通过有效的时装秀分析实现FashionReGen，涵盖时装秀理解、集体组织与分析、报告生成等关键流程。通过提出并探索这一开放型、复杂且具有领域特异性的FashionReGen任务，本文可检验大语言模型在时尚领域的通用能力，同时激发其他领域中具有工业意义的高层次任务探索。GPT-FAR的视频演示及更多材料可参见 https://github.com/CompFashion/FashionReGen。