# Face Sketch Synthesis with Style Transfer using Pyramid Column Feature

arXiv: https://arxiv.org/abs/2009.08679 · HTML: https://mlsanity.com/paper/2009.08679/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark](https://mlsanity.com/paper/2112.01019/) (2021)
- [Semi-supervised Cycle-GAN for face photo-sketch translation in the wild](https://mlsanity.com/paper/2307.10281/) (2023)
- [XCI-Sketch: Extraction of Color Information from Images for Generation of Colored Outlines and Sketches](https://mlsanity.com/paper/2108.11554/) (2021)
- [Bridging Unpaired Facial Photos And Sketches By Line-drawings](https://mlsanity.com/paper/2102.00635/) (2021)
- [PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN](https://mlsanity.com/paper/2306.13418/) (2023)
- [One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning](https://mlsanity.com/paper/2506.15312/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo. Imitating the process of how artists draw sketches, our framework synthesizes face sketches in a cascaded manner. A content image is first generated that outlines the shape of the face and the key facial features. Textures and shadings are then added to enrich the details of the sketch. We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature. We demonstrate that our style transfer approach based on the pyramid column feature can not only preserve more sketch details than the common style transfer method, but also surpasses traditional patch based methods. Quantitative and qualitative evaluations suggest that our framework outperforms other state-of-the-arts methods, and can also generalize well to different test images. Codes are available at https://github.com/chaofengc/Face-Sketch