A Survey on Personalized Content Synthesis with Diffusion Models

2024-05-09

arXiv.org

AI Summary

This survey addresses the absence of systematic reviews in personalized content synthesis (PCS), particularly regarding diffusion model applications. It is the first to focus exclusively on diffusion-based PCS, establishing a unified taxonomy that categorizes approaches into two primary paradigms: optimization-based methods (e.g., DreamBooth, LoRA fine-tuning) and learning-based methods (e.g., meta-learning, CLIP-guided alignment, multi-stage distillation). Covering core tasks—including object generation, face synthesis, and style customization—the review rigorously analyzes fundamental trade-offs among overfitting, subject fidelity, and text-image alignment. Synthesizing over 150 techniques, it constructs the first comprehensive landscape map of diffusion-driven PCS, precisely delineating performance boundaries and application scopes for each paradigm. This structured analysis provides actionable guidance for algorithm design, industrial deployment, and future academic research.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

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.

Problem

• Survey Personalized Content Synthesis methods
• Focus on diffusion models in PCS
• Analyze challenges in subject fidelity

Innovation

• Diffusion models for PCS
• Optimization-based and learning-based approaches
• Specialized tasks in PCS