LookSync: Large-Scale Visual Product Search System for AI-Generated Fashion Looks
This addresses the need for accurate product matching in AI-generated fashion for users and platforms, though it is incremental with modest improvements.
The paper tackles the problem of matching AI-generated fashion looks with real products by proposing an end-to-end search system deployed at internet scale, which serves over 350,000 AI Looks daily and improves user perception matches by 3-7% using CLIP as the backbone.
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.