A Survey on Personalized Content Synthesis with Diffusion Models
arxiv(2024)
摘要
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.
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