Abstract
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 this https URL
Abstract (translated)
图像反转是生成模型中的一个基本任务，其目标是将图像映射回它们的潜在表示形式，以便实现下游应用如编辑、恢复和风格转换。本文对最新的图像反转技术进展进行了全面回顾，并重点介绍了两大主要范式：生成对抗网络（GAN）反转和扩散模型反转。我们根据优化方法对这些技术进行分类。 对于GAN反转，我们将现有方法系统地分为基于编码器的方法、潜在空间优化方法和混合方法，分析它们的理论基础、技术创新及实际权衡。对于扩散模型反转，本文探索了无训练策略、微调方法以及额外可训练模块的设计，并突出了它们的独特优势和局限性。 此外，我们还讨论了几种流行的下游应用以及其他超越图像任务的新颖应用，识别当前面临的挑战并指出未来的研究方向。通过综合最新的发展动态，本论文旨在为研究者和实践者提供有价值的参考资源，推动该领域的进一步进步。最新工作的跟踪可以在提供的链接中找到：[此处插入具体URL]
URL
https://arxiv.org/abs/2502.11974
