LookSync: Large-Scale Visual Product Search System for AI-Generated Fashion Looks

TL;DR

LookSync is a large-scale, real-world visual product search system designed to match AI-generated fashion looks with real products, improving user experience and accuracy in fashion e-commerce.

Contribution

The paper introduces a scalable, end-to-end system for matching AI-generated fashion looks with real products, deploying CLIP-based retrieval in a production environment serving over 350,000 looks daily.

Findings

CLIP outperforms alternative models by 3-7% in mean opinion scores.

The system covers over 12 million products across global markets.

User perception matches are noticeably improved with CLIP.

Abstract

Generative AI is reshaping fashion by enabling virtual looks and avatars making it essential to find real products that best match AI-generated styles. We propose an end-to-end product search system that has been deployed in a real-world, internet scale which ensures that AI-generated looks presented to users are matched with the most visually and semantically similar products from the indexed vector space. The search pipeline is composed of four key components: query generation, vectorization, candidate retrieval, and reranking based on AI-generated looks. Recommendation quality is evaluated using human-judged accuracy scores. The system currently serves more than 350,000 AI Looks in production per day, covering diverse product categories across global markets of over 12 million products. In our experiments, we observed that across multiple annotators and categories, CLIP outperformed alternative models by a small relative margin of 3--7\% in mean opinion scores. These improvements, though modest in absolute numbers, resulted in noticeably better user perception matches, establishing CLIP as the most reliable backbone for production deployment.