# Segmentation Guided Image-to-Image Translation with Adversarial Networks

arXiv: https://arxiv.org/abs/1901.01569 · HTML: https://mlsanity.com/paper/1901.01569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis](https://mlsanity.com/paper/2205.08887/) (2022)
- [Diffusion Methods for Generating Transition Paths](https://mlsanity.com/paper/2309.10276/) (2023)
- [Bridging Day and Night: Target-Class Hallucination Suppression in Unpaired Image Translation](https://mlsanity.com/paper/2602.15383/) (2026)
- [Semantic Map Injected GAN Training for Image-to-Image Translation](https://mlsanity.com/paper/2112.01845/) (2021)

## Abstract (arXiv)

Recently image-to-image translation has received increasing attention, which aims to map images in one domain to another specific one. Existing methods mainly solve this task via a deep generative model, and focus on exploring the relationship between different domains. However, these methods neglect to utilize higher-level and instance-specific information to guide the training process, leading to a great deal of unrealistic generated images of low quality. Existing methods also lack of spatial controllability during translation. To address these challenge, we propose a novel Segmentation Guided Generative Adversarial Networks (SGGAN), which leverages semantic segmentation to further boost the generation performance and provide spatial mapping. In particular, a segmentor network is designed to impose semantic information on the generated images. Experimental results on multi-domain face image translation task empirically demonstrate our ability of the spatial modification and our superiority in image quality over several state-of-the-art methods.