# Semantics-Aware Image to Image Translation and Domain Transfer

arXiv: https://arxiv.org/abs/1904.02203 · HTML: https://mlsanity.com/paper/1904.02203/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

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

- [RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes](https://mlsanity.com/paper/2104.04606/) (2021)
- [Semantics-aware Multi-modal Domain Translation:From LiDAR Point Clouds to Panoramic Color Images](https://mlsanity.com/paper/2106.13974/) (2021)
- [Separating Content and Style for Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2110.14404/) (2021)
- [Depth- and Semantics-aware Multi-modal Domain Translation: Generating 3D Panoramic Color Images from LiDAR Point Clouds](https://mlsanity.com/paper/2302.07661/) (2023)

## Abstract (arXiv)

Image to image translation is the problem of transferring an image from a source domain to a different (but related) target domain. We present a new unsupervised image to image translation technique that leverages the underlying semantic information for object transfiguration and domain transfer tasks. Specifically, we present a generative adversarial learning approach that jointly translates images and labels from a source domain to a target domain. Our main technical contribution is an encoder-decoder based network architecture that jointly encodes the image and its underlying semantics and translates both individually to the target domain. Additionally, we propose object transfiguration and cross-domain semantic consistency losses that preserve semantic labels. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach as compared to the state-of-the-art methods on unsupervised image-to-image translation, domain adaptation, and object transfiguration.