# S-Flow GAN

arXiv: https://arxiv.org/abs/1905.08474 · HTML: https://mlsanity.com/paper/1905.08474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 1 of 1)

- [Towards Autonomous Grading In The Real World](https://mlsanity.com/paper/2206.06091/) (2022)

## Abstract (arXiv)

Our work offers a new method for domain translation from semantic label maps and Computer Graphic (CG) simulation edge map images to photo-realistic images. We train a Generative Adversarial Network (GAN) in a conditional way to generate a photo-realistic version of a given CG scene. Existing architectures of GANs still lack the photo-realism capabilities needed to train DNNs for computer vision tasks, we address this issue by embedding edge maps, and training it in an adversarial mode. We also offer an extension to our model that uses our GAN architecture to create visually appealing and temporally coherent videos.