@article{Wei2025, 
author = {Yuxiang Wei and Yiheng Zheng and Yabo Zhang and Ming Liu and Zhilong Ji and Lei Zhang and Wangmeng Zuo},
title = {Personalized image generation with deep generative models: A decade survey},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {6},
pages = {1141-1194},
keywords = {generative adversarial networks (GANs), generative models, personalized image generation, text-to-image diffusion models, multi-modal AutoRegressive models},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450495},
doi = {10.26599/CVM.2025.9450495},
abstract = {Recent advances in generative models have significantly facilitated the development of personalized content creation. Given a small set of images containing a user-specific concept, personalized image generation allows the user to create images that incorporate that concept while adhering to provided text descriptions. The technologies used for personalization have evolved alongside the development of generative models, with their distinct and interrelated components. In this survey, we present a comprehensive review of generalized personalized image generation across various generative models, including traditional GANs, contemporary text-to-image diffusion models, and emerging multi-modal autoregressive (AR) models. We first define a unified framework that standardizes the personalization process across different generative models, encompassing three key components: inversion spaces, inversion methods, and personalization schemes. This unified framework offers a structured approach to dissecting and comparing personalization techniques across different generative architectures. Building upon our framework, we provide an in-depth analysis of personalization techniques within each generative model, highlighting their unique contributions and innovations. Through comparative analysis, we elucidate the current landscape of personalized image generation, identifying commonalities and distinguishing features of existing methods. Finally, we discuss open challenges in the field and propose potential directions for future research. We keep a bibliography of related works at https://github.com/csyxwei/Awesome-Personalized-Image-Generation.}
}