PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation

Yuhang Wu, Shuxiang Zhang, Wee Hian Ching, Chi Zhang, Miao Liu
7/7/2026
cs.CV

No official code found. Best implementation: wuyuhang05/PIPBench (0 stars, MIT, last commit 2026-07-01).

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/

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Code Implementations(1)

wuyuhang05/PIPBenchCommunity69%
MIT

[ECCV2026] PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation

00Jun 22, 20263 months agoMIT

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Cite this paper

@article{wu2026pipbench,
  title  = {PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation},
  author = {Yuhang Wu and Shuxiang Zhang and Wee Hian Ching and Chi Zhang and Miao Liu},
  year   = {2026},
  eprint = {2607.06440},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.06440v1}
}

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