Image inversion is a fundamental task in generative models, aiming to mapimages back to their latent representations to enable downstream applicationssuch as editing, restoration, and style transfer. This paper provides acomprehensive review of the latest advancements in image inversion techniques,focusing on two main paradigms: Generative Adversarial Network (GAN) inversionand diffusion model inversion. We categorize these techniques based on theiroptimization methods. For GAN inversion, we systematically classify existingmethods into encoder-based approaches, latent optimization approaches, andhybrid approaches, analyzing their theoretical foundations, technicalinnovations, and practical trade-offs. For diffusion model inversion, weexplore training-free strategies, fine-tuning methods, and the design ofadditional trainable modules, highlighting their unique advantages andlimitations. Additionally, we discuss several popular downstream applicationsand emerging applications beyond image tasks, identifying current challengesand future research directions. By synthesizing the latest developments, thispaper aims to provide researchers and practitioners with a valuable referenceresource, promoting further advancements in the field of image inversion. Wekeep track of the latest works at https://github.com/RyanChenYN/ImageInversion