图像反演：从 GAN 到扩散模型及更远方向的综述
Image Inversion: A Survey from GANs to Diffusion and Beyond
AI总结：
本文系统综述 GAN 与扩散模型图像反演方法，按优化路径分类并比较技术权衡、应用与未来方向。
摘要：
图像反演是生成模型中的一项基础任务，旨在将图像映射回其潜在表示，以支持编辑、复原和风格迁移等下游应用。本文全面综述了图像反演技术的最新进展，重点关注两大范式：Generative Adversarial Network（GAN，生成对抗网络）反演和扩散模型反演。我们根据优化方法对这些技术进行分类。对于 GAN 反演，我们系统地将现有方法划分为基于编码器的方法、潜在优化方法和混合方法，分析其理论基础、技术创新和实际权衡。对于扩散模型反演，我们探讨免训练策略、微调方法以及额外可训练模块的设计，强调其独特优势和局限性。此外，我们讨论了若干流行的下游应用以及超越图像任务的新兴应用，指出当前挑战和未来研究方向。通过综合最新发展，本文旨在为研究人员和从业者提供有价值的参考资源，推动图像反演领域的进一步发展。我们在 https://github.com/RyanChenYN/ImageInversion 持续追踪最新工作。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
