Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs
This addresses a niche problem in fashion recommendation for customers interested in character-based styling, but it is incremental as it builds on existing LLM and text-to-image methods.
The paper tackles the problem of generating outfits based on customer interests in characters from media, proposing a framework that uses LLMs and text-to-image models to interpret character information and produce personalized recommendations, with experiments showing effectiveness across multiple dimensions.
The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or specific query styles but do not consider customer interests in famous characters from movie, social media, etc. In this paper, we define a new Character-based Outfit Generation (COG) problem, designed to accurately interpret character information and generate complete outfit sets according to customer specifications such as age and gender. To tackle this problem, we propose a novel framework LVA-COG that leverages Large Language Models (LLMs) to extract insights from customer interests (e.g., character information) and employ prompt engineering techniques for accurate understanding of customer preferences. Additionally, we incorporate text-to-image models to enhance the visual understanding and generation (factual or counterfactual) of cohesive outfits. Our framework integrates LLMs with text-to-image models and improves the customer's approach to fashion by generating personalized recommendations. With experiments and case studies, we demonstrate the effectiveness of our solution from multiple dimensions.