Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914677166964736 |
|---|---|
| author | Forouzandehmehr, Najmeh Cao, Yijie Thakurdesai, Nikhil Giahi, Ramin Ma, Luyi Farrokhsiar, Nima Xu, Jianpeng Korpeoglu, Evren Achan, Kannan |
| author_facet | Forouzandehmehr, Najmeh Cao, Yijie Thakurdesai, Nikhil Giahi, Ramin Ma, Luyi Farrokhsiar, Nima Xu, Jianpeng Korpeoglu, Evren Achan, Kannan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05941 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs Forouzandehmehr, Najmeh Cao, Yijie Thakurdesai, Nikhil Giahi, Ramin Ma, Luyi Farrokhsiar, Nima Xu, Jianpeng Korpeoglu, Evren Achan, Kannan Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs |
| topic | Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.05941 |