Query-Grounded Preference-Aware Personalized Sticker Generation

Personalized sticker generation with intent aims to create stickers that reflect both a user's visual preferences and semantic intent. Existing reference-aware approaches rely on sticker proxies to infer semantic intent, which may dilute intent extraction when suitable visual exemplars are unavailable. We propose Query-Grounded Preference-Aware Personalized Sticker Generation (QuPA-StickerGen), a framework that directly incorporates the user's textual query. First, we aggregate reusable style adapters with supervision from a vision-language model. Second, user sticker usage preferences and textual queries are mapped into a shared style space. Third, both signals are adaptively fused according to query confidence weight. This enables the generation of stickers that better align with both how users prefer content to look and what they want to express. Experiments show that our approach outperforms existing methods in both user stylistic preference and semantic alignment. Dataset and code are released.