# Unpaired Image-to-Image Translation with Content Preserving Perspective: A Review

arXiv: https://arxiv.org/abs/2502.08667 · HTML: https://mlsanity.com/paper/2502.08667/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation](https://mlsanity.com/paper/2503.15056/) (2025)
- [REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework](https://mlsanity.com/paper/2508.17061/) (2025)
- [Structure-Preserving Unpaired Image Translation to Photometrically Calibrate JunoCam with Hubble Data](https://mlsanity.com/paper/2511.22668/) (2025)
- [Vectra: A New Metric, Dataset, and Model for Visual Quality Assessment in E-Commerce In-Image Machine Translation](https://mlsanity.com/paper/2602.07014/) (2026)
- [FD-DB: Frequency-Decoupled Dual-Branch Network for Unpaired Synthetic-to-Real Domain Translation](https://mlsanity.com/paper/2602.09476/) (2026)

## Abstract (arXiv)

Image-to-image translation (I2I) transforms an image from a source domain to a target domain while preserving source content. Most computer vision applications are in the field of image-to-image translation, such as style transfer, image segmentation, and photo enhancement. The degree of preservation of the content of the source images in the translation process can be different according to the problem and the intended application. From this point of view, in this paper, we divide the different tasks in the field of image-to-image translation into three categories: Fully Content preserving, Partially Content preserving, and Non-Content preserving. We present different tasks, datasets, methods, results of methods for these three categories in this paper. We make a categorization for I2I methods based on the architecture of different models and study each category separately. In addition, we introduce well-known evaluation criteria in the I2I translation field. Specifically, nearly 70 different I2I models were analyzed, and more than 10 quantitative evaluation metrics and 30 distinct tasks and datasets relevant to the I2I translation problem were both introduced and assessed. Translating from simulation to real images could be well viewed as an application of fully content preserving or partially content preserving unsupervised image-to-image translation methods. So, we provide a benchmark for Sim-to-Real translation, which can be used to evaluate different methods. In general, we conclude that because of the different extent of the obligation to preserving content in various applications, it is better to consider this issue in choosing a suitable I2I model for a specific application.