Image editing aims to edit the given synthetic or real image to meet the
specific requirements from users. It is widely studied in recent years as a
promising and challenging field of Artificial Intelligence Generative Content
(AIGC). Recent significant advancement in this field is based on the
development of text-to-image (T2I) diffusion models, which generate images
according to text prompts. These models demonstrate remarkable generative
capabilities and have become widely used tools for image editing. T2I-based
image editing methods significantly enhance editing performance and offer a
user-friendly interface for modifying content guided by multimodal inputs. In
this survey, we provide a comprehensive review of multimodal-guided image
editing techniques that leverage T2I diffusion models. First, we define the
scope of image editing from a holistic perspective and detail various control
signals and editing scenarios. We then propose a unified framework to formalize
the editing process, categorizing it into two primary algorithm families. This
framework offers a design space for users to achieve specific goals.
Subsequently, we present an in-depth analysis of each component within this
framework, examining the characteristics and applicable scenarios of different
combinations. Given that training-based methods learn to directly map the
source image to target one under user guidance, we discuss them separately, and
introduce injection schemes of source image in different scenarios.
Additionally, we review the application of 2D techniques to video editing,
highlighting solutions for inter-frame inconsistency. Finally, we discuss open
challenges in the field and suggest potential future research directions. We
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图像编辑以满足用户特定需求为目标，近年来作为人工智能生成内容 (AIGC) 领域的一项有前景且具有挑战性的研究得到广泛关注。本篇综述针对利用文本转图像扩散模型进行多模态引导的图像编辑技术进行了全面回顾，介绍了综合的图像编辑范畴、各种控制信号和编辑场景，提出了一个统一的框架来规范编辑过程，并分成两个主要算法体系，为用户实现特定目标提供了一个设计空间。另外，对于基于训练的方法，我们讨论了它们的特点和适用场景，并介绍了在不同场景下源图像注入的方案。此外，我们还回顾了将二维技术应用于视频编辑，并突出了解决帧间不一致问题的解决方案。最后，我们讨论了该领域面临的开放性挑战，并提出了潜在的未来研究方向。