Zhan Fangneng, Yu Yingchen, Wu Rongliang, Zhang Jiahui, Lu Shijian, Liu Lingjie, Kortylewski Adam, Theobalt Christian, Xing Eric
IEEE Trans Pattern Anal Mach Intell. 2023 Dec;45(12):15098-15119. doi: 10.1109/TPAMI.2023.3305243. Epub 2023 Nov 3.
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.
由于现实世界中的信息以多种模态存在，多模态信息之间的有效交互与融合在计算机视觉和深度学习研究中对于多模态数据的创建与感知起着关键作用。凭借在对多模态信息之间的交互进行建模方面的强大能力，多模态图像合成与编辑近年来已成为一个热门研究课题。多模态引导并非为网络训练提供明确指导，而是为图像合成与编辑提供直观且灵活的方式。另一方面，该领域在多模态特征对齐、高分辨率图像合成、可靠的评估指标等方面也面临若干挑战。在本综述中，我们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制定了分类法。我们首先介绍图像合成与编辑中的不同引导模态，然后根据模型类型广泛描述多模态图像合成与编辑方法。之后，我们描述基准数据集、评估指标以及相应的实验结果。最后，我们对当前的研究挑战以及未来研究的可能方向给出见解。
