Authors
Yingxue Pang, Jianxin Lin, Tao Qin, Zhibo Chen
University of Science and Technology of China
Portals
Summary
In this paper, we aim to provide a comprehensive review of the recent progress in image-to-image translation research works. To the best of our knowledge, this is the first overview paper to cover the analysis, methodology, and related applications of I2I.
Abstract
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems, such as image synthesis, segmentation, style transfer, restoration, and pose estimation. In this paper, we provide an overview of the I2I works developed in recent years. We will analyze the key techniques of the existing I2I works and clarify the main progress the community has made. Additionally, we will elaborate on the effect of I2I on the research and industry community and point out remaining challenges in related fields.
Contribution
- We briefly introduce the two most representative and commonly adopted generative models, as well as some well-known evaluation metrics, applied for image-to-image translation, and then we analyze how these generative models learn to represent and acquire the desired translation results
- We categorize the I2I problem into two main sets of tasks, i.e., two-domain I2I tasks and multi-domain I2I tasks, in which numerous I2I works have appeared for each set of I2I tasks and brought far-reaching influence on other research fields
- We provide a thorough taxonomy of the I2I applications following the same categorizations of I2I methods