Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
Chaofeng Chen, Xiao Tan, and Kwan-Yee K. Wong

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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · Advanced Image Processing Techniques
