用AI生成穿搭后，自动匹配最相似的实物商品。
LookSync: Large-Scale Visual Product Search System for AI-Generated Fashion Looks
- 基于AI生成穿搭图，通过向量化检索匹配真实商品
- 日均服务35万+穿搭，覆盖超1200万种商品
- CLIP模型表现最优，提升用户感知匹配度
生成式AI正在重塑时尚产业，使虚拟穿搭和数字形象成为可能，因此精准匹配真实商品至关重要。本文提出一个端到端的商品搜索系统，已在实际互联网规模部署，确保向用户展示的AI生成穿搭能与索引向量空间中视觉和语义最相似的商品匹配。该系统包含四个关键组件：查询生成、向量化、候选检索和基于AI生成穿搭的重排序。推荐质量通过人工评分的准确率评估。目前系统每日服务超过35万次AI穿搭，覆盖全球市场中超过1200万种商品。实验表明，在多个标注者和品类下，CLIP模型在平均意见得分上优于其他模型3–7%，虽绝对提升较小，但显著提升了用户感知匹配度，确立其作为生产环境首选骨干模型的地位。
原文摘要 · Abstract (English)
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.
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