Image inversion is a fundamental task in generative models, aiming to map<br>images back to their latent representations to enable downstream applications<br>such as editing, restoration, and style transfer. This paper provides a<br>comprehensive review of the latest advancements in image inversion techniques,<br>focusing on two main paradigms: Generative Adversarial Network (GAN) inversion<br>and diffusion model inversion. We categorize these techniques based on their<br>optimization methods. For GAN inversion, we systematically classify existing<br>methods into encoder-based approaches, latent optimization approaches, and<br>hybrid approaches, analyzing their theoretical foundations, technical<br>innovations, and practical trade-offs. For diffusion model inversion, we<br>explore training-free strategies, fine-tuning methods, and the design of<br>additional trainable modules, highlighting their unique advantages and<br>limitations. Additionally, we discuss several popular downstream applications<br>and emerging applications beyond image tasks, identifying current challenges<br>and future research directions. By synthesizing the latest developments, this<br>paper aims to provide researchers and practitioners with a valuable reference<br>resource, promoting further advancements in the field of image inversion. We<br>keep track of the latest works at https://github.com/RyanChenYN/ImageInversion