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.

翻译：近年来，生成模型的进展显著影响了内容创作，催生了个性化内容合成（PCS）这一方向。PCS旨在利用用户提供的一组少量示例，针对特定用户定义的提示词定制感兴趣的主题。过去两年中，已提出超过150种方法。然而，现有综述主要关注文本到图像生成，鲜有提供PCS的最新总结。本文对PCS进行了全面综述，特别聚焦于扩散模型。具体而言，我们介绍了PCS研究的通用框架，这些框架可大致分为基于优化的方法和基于学习的方法。我们进一步对这些方法进行分类与剖析，探讨其优势、局限性和关键技术。此外，我们深入探讨了该领域的专门任务，如个性化物体生成、人脸合成与风格个性化，突出其独特挑战与创新。尽管取得了令人鼓舞的进展，我们也分析了过拟合、主体保真度与文本对齐之间的权衡等挑战。通过上述详细概述与分析，我们提出了推动PCS发展的未来方向。