As information exists in various modalities in real world, effective interaction and fusion among multimodal information plays a key role for the creation and perception of multimodal data in computer vision and deep learning research. With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years. Instead of providing explicit guidance for network training, multimodal guidance offers intuitive and flexible means for image synthesis and editing. On the other hand, this field is also facing several challenges in alignment of multimodal features, synthesis of high-resolution images, faithful evaluation metrics, etc. In this survey, we comprehensively contextualize the advance of the recent multimodal image synthesis and editing and formulate taxonomies according to data modalities and model types. We start with an introduction to different guidance modalities in image synthesis and editing, and then describe multimodal image synthesis and editing approaches extensively according to their model types. After that, we describe benchmark datasets and evaluation metrics as well as corresponding experimental results. Finally, we provide insights about the current research challenges and possible directions for future research. A project associated with this survey is available at https://github.com/fnzhan/Generative-AI.

翻译：现实世界中信息以多种模态存在，多模态信息间的有效交互与融合对计算机视觉和深度学习研究中多模态数据的生成与感知起着关键作用。凭借对多模态信息交互建模的卓越能力，多模态图像合成与编辑近年来成为研究热点。与为网络训练提供显式指导不同，多模态引导为图像合成与编辑提供了直观灵活的手段。另一方面，该领域仍面临多模态特征对齐、高分辨率图像生成、可靠评估指标等若干挑战。本综述全面梳理了近期多模态图像合成与编辑的进展，并根据数据模态和模型类型构建分类体系。我们首先介绍图像合成与编辑中的不同引导模态，随后根据模型类型详尽阐述多模态图像合成与编辑方法。接着，描述基准数据集和评估指标及相应实验结果。最后，我们针对当前研究挑战和未来可能的研究方向提出见解。本综述的相关项目可访问 https://github.com/fnzhan/Generative-AI。