A Survey on Personalized Content Synthesis with Diffusion Models

Authors: Xulu Zhang, Xiao-Yong Wei, Wengyu Zhang, Jinlin Wu, Zhaoxiang Zhang, Zhen Lei, Qing Li

What

This paper presents a comprehensive survey of Personalized Content Synthesis (PCS) with diffusion models, focusing on techniques that enable the generation of customized images based on user-provided references and text prompts.

Why

This survey is important due to the rapid growth and significance of PCS in various applications, including content creation, digital marketing, and virtual reality. It provides a timely and comprehensive overview of this evolving field, analyzing different frameworks, specialized tasks, and future challenges.

How

The paper categorizes PCS approaches into optimization-based and learning-based methods, analyzing their strengths and limitations. It reviews specialized tasks like object, style, and face personalization, highlighting key techniques like attention manipulation and mask-guided generation.

Result

The survey reveals significant progress in PCS, with methods achieving impressive results in generating personalized content. It identifies key techniques like attention-based operations, mask-guided generation, data augmentation, and regularization as crucial for improving PCS performance. The paper also provides a comparative analysis of different PCS methods and their performance on benchmark datasets.

LF

The paper identifies key challenges in PCS, including overfitting to limited references, balancing subject fidelity with text alignment, and the lack of standardized evaluation metrics and datasets. It suggests future research directions, such as exploring new architectures, training methodologies, and robust evaluation techniques to address these limitations.

Abstract

Recent advancements in generative models have significantly impacted content creation, leading to the emergence of Personalized Content Synthesis (PCS). With a small set of user-provided examples, PCS aims to customize the subject of interest to specific user-defined prompts. Over the past two years, more than 150 methods have been proposed. However, existing surveys mainly focus on text-to-image generation, with few providing up-to-date summaries on PCS. This paper offers a comprehensive survey of PCS, with a particular focus on the diffusion models. Specifically, we introduce the generic frameworks of PCS research, which can be broadly classified into optimization-based and learning-based approaches. We further categorize and analyze these methodologies, discussing their strengths, limitations, and key techniques. Additionally, we delve into specialized tasks within the field, such as personalized object generation, face synthesis, and style personalization, highlighting their unique challenges and innovations. Despite encouraging progress, we also present an analysis of the challenges such as overfitting and the trade-off between subject fidelity and text alignment. Through this detailed overview and analysis, we propose future directions to advance the development of PCS.