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A Survey on Personalized Content Synthesis with Diffusion Models

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  • Published: 27 September 2025
  • Volume 22, pages 817–848, (2025)
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Machine Intelligence Research Aims and scope Submit manuscript
A Survey on Personalized Content Synthesis with Diffusion Models
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  • Xulu Zhang  ORCID: orcid.org/0000-0003-2473-460X1,2,
  • Xiaoyong Wei  ORCID: orcid.org/0000-0002-5706-51771,
  • Wentao Hu  ORCID: orcid.org/0000-0002-2071-93411,
  • Jinlin Wu  ORCID: orcid.org/0000-0001-7877-57282,3,
  • Jiaxin Wu  ORCID: orcid.org/0000-0003-4074-34421,
  • Wengyu Zhang  ORCID: orcid.org/0009-0001-2347-41831,
  • Zhaoxiang Zhang  ORCID: orcid.org/0000-0003-2648-38752,3,4,
  • Zhen Lei  ORCID: orcid.org/0000-0002-0791-189X2,3,4 &
  • …
  • Qing Li  ORCID: orcid.org/0000-0003-3370-471X1 

  • 1371 Accesses

  • 3 Citations

  • 1 Altmetric

  • Explore all metrics

Abstract

Recent advancements in diffusion models have significantly impacted content creation, leading to the emergence of personalized content synthesis (PCS). By utilizing a small set of user-provided examples featuring the same subject, PCS aims to tailor this subject to specific user-defined prompts. Over the past two years, more than 150 methods have been introduced in this area. However, existing surveys primarily focus on text-to-image generation, with few providing up-to-date summaries on PCS. This paper provides a comprehensive survey of PCS, introducing the general frameworks of PCS research, which can be categorized into test-time fine-tuning (TTF) and pre-trained adaptation (PTA) approaches. We analyze the strengths, limitations and key techniques of these methodologies. Additionally, we explore specialized tasks within the field, such as object, face and style personalization, while highlighting their unique challenges and innovations. Despite the promising progress, we also discuss ongoing challenges, including overfitting and the trade-off between subject fidelity and text alignment. Through this detailed overview and analysis, we propose future directions to further the development of PCS.

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Acknowledgements

This work was supported in part by Chinese National Natural Science Foundation Projects, China (Nos. U23B2054, 62276254 and 62372314), Beijing Natural Science Foundation, China (No. L221013), InnoHK program, and Hong Kong Research Grants Council through Research Impact Fund, China (No. R1015-23). Open access funding provided by The Hong Kong Polytechnic University, China.

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Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, 999077, China

    Xulu Zhang, Xiaoyong Wei, Wentao Hu, Jiaxin Wu, Wengyu Zhang & Qing Li

  2. Center for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, 999077, China

    Xulu Zhang, Jinlin Wu, Zhaoxiang Zhang & Zhen Lei

  3. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Jinlin Wu, Zhaoxiang Zhang & Zhen Lei

  4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhaoxiang Zhang & Zhen Lei

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  1. Xulu Zhang
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Correspondence to Xiaoyong Wei or Zhen Lei.

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Xulu Zhang received the B. Eng. and the M. Sc. degrees in computer science from Sichuan University, China in 2019 and 2022. He is a Ph. D. degree candidate from the Department of Computing, The Hong Kong Polytechnic University, China.

His research interests include image generation and active learning.

Xiaoyong Wei received the Ph. D. degree in computer science from the City University of Hong Kong, China in 2009, and has worked as a postdoctoral fellow in the University of California, USA from December 2013 to December 2015. He has been a professor and the head of Department of Computer Science, Sichuan University, China since 2010. He is an adjunct professor of Peng Cheng Laboratory, China, and a visiting professor of Department of Computing, The Hong Kong Polytechnic University, China. He is a senior member of IEEE, and has served as an Associate Editor of Interdisciplinary Sciences: Computational Life Sciences since 2020, the program Chair of ICMR 2019, ICIMCS 2012, and the technical committee member of over 20 conferences such as ICCV, CVPR, SIGKDD, ACM MM, ICME, and ICIP.

His research interests include multimedia computing, health computing, machine learning and large-scale data mining.

Wentao Hu received the B. Eng. degree in computer science from Shandong University, China in 2021, and the M. Sc. degree in computer science from Sun Yatsen University, China in 2024. He is currently a Ph. D. degree candidate from the Department of Computing, The Hong Kong Polytechnic University, China.

His research interests include image generation and 3D reconstruction.

Jinlin Wu received the B. Sc. degree in computer science from the University of Electronic Science and Technology of China, China in 2017, and the Ph. D. degree in computer science from the University of Chinese Academy of Sciences, China in 2022. He is an assistant research fellow at the Institute of Automation, Chinese Academy of Sciences (CAS), China. He has served as the principal investigator of a National Natural Science Foundation of China (NSFC) Youth Science Fund project and has participated in several other NSFC-funded projects. He has accumulated a solid research foundation in areas such as video analysis for security and medical video understanding. He has published over 30 high-quality academic papers, with more than 500 citations.

His research interests include object detection, image recognition, and video understanding.

Jiaxin Wu received the Ph. D. degree in computer science from the City University of Hong Kong, China in 2024. She is a postdoctoral fellow in the Department of Computing at The Hong Kong Polytechnic University, China.

Her research interests include multimedia retrieval, AI for Science (AI4Science), and natural language processing (NLP).

Wengyu Zhang is an undergraduate student in the Department of Computing, The Hong Kong Polytechnic University, China.

His research interests include natural language processing (NLP), AI for Science (AI4Science), and graph learning.

Zhaoxiang Zhang received the B. Sc. degree in computer science in Department of Electronic Science and Technology, University of Science and Technology of China, China in 2004, and the Ph. D. degree in pattern recognition and intelligent systems from the Institute of Automation, Chinese Academy of Sciences, China in 2009, respectively. From 2009 to 2015, he worked as a lecturer, associate professor, and later the deputy director of Department of Computer Application Technology at the Beihang University, China. Since July 2015, he has joined the Institute of Automation, Chinese Academy of Sciences, where he is currently a professor. Recently, he specifically focuses on deep learning models, biologically-inspired visual computing and human-like learning, and their applications on human analysis and scene understanding. He has published more than 200 papers in international journals and conferences, including reputable international journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, Journal of Machine Learning Research, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, and top-level international conferences like CVPR, ICCV, NIPS, ECCV, ICLR, AAAI, IJCAI and ACM MM. He has won the best paper awards in several conferences and championships in international competitions and his research has won the “Technical Innovation Award of the Chinese Association of Artificial Intelligence”. He has served as the PC Chair or Area Chair of many international conferences like CVPR, ICCV, AAAI, IJCAI, ACM MM, ICPR and BICS. He is serving or has served as Associate Editor of reputable international journals like International Journal of Computer Vision, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Biometrics, Behavior, and Identity Science, Pattern Recognition and Neurocomputing.

His research interests include pattern recognition, computer vision and machine learning.

Zhen Lei received the B. Sc. degree in automation from the University of Science and Technology of China, China in 2005, and the Ph. D. degree in computer vision from the Institute of Automation, Chinese Academy of Sciences, China in 2010, where he is currently a professor. He is IEEE Fellow, IAPR Fellow and AAIA Fellow. He has published over 200 papers in international journals and conferences with 33 000+ citations in Google Scholar and h-index 86. He was the program co-chair of IJCB2023, was competition co-chair of IJCB2022 and has served as Area Chairs for several conferences and is an Associate Editor for IEEE Transactions on Information Forensics and Security, IEEE Transactions on Biometrics, Behavior, and Identity Science, Pattern Recognition, Neurocomputing and IET Computer Vision journals. He is the winner of 2019 IAPR Young Biometrics Investigator Award.

His research interests include computer vision, pattern recognition, image processing, and face recognition in particular.

Qing Li received the Ph. D. degree in data science from University of Southern California, USA in 1989. He is currently a Chair professor (Data Science) and the Head of the Department of Computing, The Hong Kong Polytechnic University. Formerly, he was the founding director of the Multimedia software Engineering Research Centre (MERC), and a professor at City University of Hong Kong where he worked in the Department of Computer Science from 1998 to 2018. Prior to these, he has also taught at the Hong Kong University of Science and Technology and the Australian National University (Canberra, Australia). He served as a consultant to Microsoft Research Asia (Beijing, China), Motorola Global Computing and Telecommunications Division (Tianjin Regional Operations Center), and the Division of Information Technology, Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. He has been an adjunct professor of the University of Science and Technology of China (USTC) and the Wuhan University, and a guest professor of the Hunan University (Changsha, China) where he got his B. Eng. degree from the Department of Computer Science in 1982. He is also a guest professor (Software Technology) of the Zhejiang University (Hangzhou, China) - the leading university of the Zhejiang province where he was born.

He has been actively involved in the research community by serving as an Associate Editor and reviewer for technical journals, and as an organizer/co-organizer of numerous international conferences. Some recent conferences in which he is playing or has played major roles include APWeb-WAIM2018, ICDM 2018, WISE2017, ICDSC2016, DASFAA2015, U-Media2014, ER2013, RecSys2013, NDBC2012, ICMR2012, CoopIS2011, WAIM2010, DASFAA2010, APWeb-WAIM2009, ER2008, WISE2007, ICWL2006, HSI2005, WAIM2004, IDEAS2003, VLDB2002, PAKDD2001, IFIP 2.6 Working Conference on Database Semantics (DS-9), IDS2000, and WISE2000. In addition, he served as a programme committee member for over fifty international conferences (including VLDB, ICDE, WWW, DASFAA, ER, CIKM, CAiSE, CoopIS, and FODO). He is currently a fellow of IEEE and IET/IEE, a member of ACM-SIGMOD and IEEE Technical Committee on Data Engineering. He is the Chairperson of the Hong Kong Web Society, and also served/is serving as an executive committee (EXCO) member of IEEE-Hong Kong Computer Chapter and ACM Hong Kong Chapter. In addition, he serves as a councilor of the Database Society of Chinese Computer Federation (CCF), a member of the Big Data Expert Committee of CCF, and is a Steering Committee member of DASFAA, ER, ICWL, UMEDIA, and WISE Society.

His research interests include data science and artificial intelligence.

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Zhang, X., Wei, X., Hu, W. et al. A Survey on Personalized Content Synthesis with Diffusion Models. Mach. Intell. Res. 22, 817–848 (2025). https://doi.org/10.1007/s11633-025-1563-3

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  • Received: 07 February 2025

  • Accepted: 13 May 2025

  • Published: 27 September 2025

  • Version of record: 27 September 2025

  • Issue date: October 2025

  • DOI: https://doi.org/10.1007/s11633-025-1563-3

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Keywords

  • Generative models
  • image synthesis
  • diffusion models
  • personalized content synthesis
  • subject customization

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  1. Xiaoyong Wei View author profile
  2. Qing Li View author profile

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