Face Sketch Synthesis with Style Transfer using Pyramid Column Feature

TL;DR

This paper introduces a deep neural network framework for face sketch synthesis that mimics artistic drawing by generating a content outline and then adding textures using a novel pyramid column feature style transfer, outperforming existing methods.

Contribution

The paper presents a new style transfer approach with pyramid column features that better preserves sketch details and surpasses traditional patch-based methods.

Findings

Outperforms state-of-the-art face sketch synthesis methods

Preserves more sketch details with pyramid column feature style transfer

Generalizes well to different test images

Abstract

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