# High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks

arXiv: https://arxiv.org/abs/1710.10182 · HTML: https://mlsanity.com/paper/1710.10182/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2017.

## Built on by (most cited first, 11 of 11)

- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [Facial-Sketch Synthesis: A New Challenge](https://mlsanity.com/paper/2112.15439/) (2021)
- [FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer](https://mlsanity.com/paper/2112.00281/) (2021)
- [Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel Prior](https://mlsanity.com/paper/2404.01703/) (2024)
- [Multimodal Face Synthesis from Visual Attributes](https://mlsanity.com/paper/2104.04362/) (2021)
- [Semi-supervised Cycle-GAN for face photo-sketch translation in the wild](https://mlsanity.com/paper/2307.10281/) (2023)
- [Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning](https://mlsanity.com/paper/2201.01592/) (2022)
- [Bridging Unpaired Facial Photos And Sketches By Line-drawings](https://mlsanity.com/paper/2102.00635/) (2021)
- [Face sketch to photo translation using generative adversarial networks](https://mlsanity.com/paper/2110.12290/) (2021)

## Abstract (arXiv)

Synthesizing face sketches from real photos and its inverse have many applications. However, photo/sketch synthesis remains a challenging problem due to the fact that photo and sketch have different characteristics. In this work, we consider this task as an image-to-image translation problem and explore the recently popular generative models (GANs) to generate high-quality realistic photos from sketches and sketches from photos. Recent GAN-based methods have shown promising results on image-to-image translation problems and photo-to-sketch synthesis in particular, however, they are known to have limited abilities in generating high-resolution realistic images. To this end, we propose a novel synthesis framework called Photo-Sketch Synthesis using Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution to high resolution images in an adversarial way. The hidden layers of the generator are supervised to first generate lower resolution images followed by implicit refinement in the network to generate higher resolution images. Furthermore, since photo-sketch synthesis is a coupled/paired translation problem, we leverage the pair information using CycleGAN framework. Both Image Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to demonstrate the superior performance of our framework in comparison to existing state-of-the-art solutions. Code available at: https://github.com/lidan1/PhotoSketchMAN.