  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.
