{"ID":6023645,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T16:04:53.245822622Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06440","arxiv_id":"2607.06440","title":"PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation","abstract":"Recent text-to-image models such as DALLE-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personalized image generation, where models must align outputs with a user's implicit visual preferences based on a few historically preferred images and a short prompt. To this end, we introduce PIPBench, the first profile-inclusive benchmark for evaluating personalized image generation. We further propose a novel data construction pipeline that leverages psychological and demographic profiling dimensions for both real-user data collection and scalable agent-based data generation. Using PIPBench, we conduct a thorough evaluation of representative line of methods. Our experiments reveal key limitations in existing methods, suggesting new challenges and opportunities for personalized text-to-image synthesis. Project page: https://wuyuhang05.github.io/PIPBench/","short_abstract":"Recent text-to-image models such as DALLE-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personalized image generation, where models must align outputs with a user's implicit visual preferences based on a few historically preferred images and a short prompt. To this...","url_abs":"https://arxiv.org/abs/2607.06440","url_pdf":"https://arxiv.org/pdf/2607.06440v1","authors":"[\"Yuhang Wu\",\"Shuxiang Zhang\",\"Wee Hian Ching\",\"Chi Zhang\",\"Miao Liu\"]","published":"2026-07-07T16:09:30Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}