Character-based Outfit Generation with Vision-augmented Style Extraction
  via LLMs
Najmeh Forouzandehmehr Yijie Cao Nikhil Thakurdesai Ramin Giahi Luyi Ma Nima Farrokhsiar Jianpeng Xu Evren Korpeoglu Kannan Achan
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.