Abstract
Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficult to meet users’ varied content needs. To address this limitation, personalized content generation has emerged as a promising direction with broad applications. Nevertheless, most existing research focuses on personalized text generation, with relatively little attention given to personalized image generation. The limited work in personalized image generation faces challenges in accurately capturing users’ visual preferences and needs from noisy user-interacted images and complex multimodal instructions. Worse still, there is a lack of supervised data for training personalized image generation models. To overcome the challenges, we propose a Personalized Image Generation Framework named Pigeon, which adopts exceptional large multimodal models with three dedicated modules to capture users’ visual preferences and needs from noisy user history and multimodal instructions. To alleviate the data scarcity, we introduce a two-stage preference alignment scheme, comprising masked preference reconstruction and pairwise preference alignment, to align Pigeon with the personalized image generation task. We apply Pigeon to personalized sticker and movie poster generation, where extensive quantitative results and human evaluation highlight its superiority over various generative baselines.
Citation:
@inproceedings{xu2025pigeon,
title={Personalized Image Generation with Large Multimodal Models},
author={Xu, Yiyan and Wang, Wenjie and Zhang, Yang and Tang, Biao and Yan, Peng and Feng, Fuli and He, Xiangnan},
booktitle={Proceedings of the ACM Web Conference 2025},
year={2025}
}
Yiyan Xu
许艺龑
Wenjie Wang
王文杰 教授
Fuli Feng
冯福利 教授
