# Bridging Unpaired Facial Photos And Sketches By Line-drawings

arXiv: https://arxiv.org/abs/2102.00635 · HTML: https://mlsanity.com/paper/2102.00635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://mlsanity.com/paper/1907.10830/) (2019)
- [Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2003.00273/) (2020)
- [High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks](https://mlsanity.com/paper/1710.10182/) (2017)
- [Scoot: A Perceptual Metric for Facial Sketches](https://mlsanity.com/paper/1908.08433/) (2019)
- [Semi-Supervised Learning for Face Sketch Synthesis in the Wild](https://mlsanity.com/paper/1812.04929/) (2018)
- [Face Sketch Synthesis with Style Transfer using Pyramid Column Feature](https://mlsanity.com/paper/2009.08679/) (2020)
- [Making Robots Draw A Vivid Portrait In Two Minutes](https://mlsanity.com/paper/2005.05526/) (2020)

## Abstract (arXiv)

In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain X and the sketch domain Y by using the line-drawing domain Z. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. F: X/Y mapsto Z. Consequently, we obtain textitpseudo paired data (Z, Y), and can learn the mapping G:Z mapsto Y in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by G circ F. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists' rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods.