系统梳理从GAN到扩散模型的图像反演技术，助力编辑与修复应用。
Image Inversion: A Survey from GANs to Diffusion and Beyond
- 按优化方式分类GAN与扩散模型的反演方法
- 涵盖编码器、隐空间优化及可训练模块等核心技术
- 适合生成模型研究者和图像编辑开发者参考
图像反演是生成模型中的基础任务，旨在将图像映射回其潜在表示，以支持编辑、修复和风格迁移等下游应用。本文全面回顾了图像反演技术的最新进展，重点聚焦生成对抗网络（GAN）反演与扩散模型反演两大范式。根据优化方法对现有技术进行分类：对于GAN反演，系统划分为基于编码器的方法、隐空间优化方法及混合方法，并分析其理论基础、技术创新与实际权衡；对于扩散模型反演，探讨无训练策略、微调方法及额外可训练模块的设计，突出各自优势与局限。此外，讨论了主流下游应用及跨图像任务的新方向，识别当前挑战与未来研究路径。通过整合最新成果，本综述旨在为研究人员与实践者提供有价值的参考资料，推动该领域持续发展。最新工作动态可关注 https://github.com/RyanChenYN/ImageInversion。
原文摘要 · Abstract (English)
Image inversion is a fundamental task in generative models, aiming to map images back to their latent representations to enable downstream applications such as editing, restoration, and style transfer. This paper provides a comprehensive review of the latest advancements in image inversion techniques, focusing on two main paradigms: Generative Adversarial Network (GAN) inversion and diffusion model inversion. We categorize these techniques based on their optimization methods. For GAN inversion, we systematically classify existing methods into encoder-based approaches, latent optimization approaches, and hybrid approaches, analyzing their theoretical foundations, technical innovations, and practical trade-offs. For diffusion model inversion, we explore training-free strategies, fine-tuning methods, and the design of additional trainable modules, highlighting their unique advantages and limitations. Additionally, we discuss several popular downstream applications and emerging applications beyond image tasks, identifying current challenges and future research directions. By synthesizing the latest developments, this paper aims to provide researchers and practitioners with a valuable reference resource, promoting further advancements in the field of image inversion. We keep track of the latest works at https://github.com/RyanChenYN/ImageInversion
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