PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation
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
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- PossiblePossibly related (embedding) · 46%Meta just launched a new AI generator, Muse Image, and users are already pushing back over use of their photos →
- LinkedLinked via arxiv author · 85%Yuhang Wu →
“PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation”
- LinkedLinked via arxiv author · 85%Shuxiang Zhang →
“PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation”
- LinkedLinked via arxiv author · 85%Wee Hian Ching →
“PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation”
- LinkedLinked via arxiv author · 85%Chi Zhang →
“PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation”
- LinkedLinked via arxiv author · 85%Miao Liu →
“PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation”