CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Facial-sketch Synthesis: A New Challenge
Deng-Ping Fan1; Ziling Huang2; Peng Zheng3; Hong Liu4; Xuebin Qin3; Luc Van Gool1
Source PublicationMachine Intelligence Research

This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of obtaining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS investigation by reviewing 89 classic methods, including 25 handcrafted feature-based facial-sketch synthesis approaches, 29 general translation methods, and 35 image-to-sketch approaches. In addition, we elaborate comprehensive experiments on the existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facialaware masking and style-vector expansion, our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin. Finally, we conclude with lessons learned over the past years and point out several unsolved challenges. Our code is available at

KeywordFacial sketch synthesis (FSS) facial sketch dataset benchmark attribute style transfer
Sub direction classification其他
planning direction of the national heavy laboratory其他
Paper associated data
Chinese guide
Video parsing
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.Computer Vision Laboratory, ETH Zürich, Zürich 8092, Switzerland
2.Information and Communication Engineering, University of Tokyo, Tokyo 113-8654, Japan
3.Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
4.Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo 101-8430, Japan
Recommended Citation
GB/T 7714
Deng-Ping Fan,Ziling Huang,Peng Zheng,et al. Facial-sketch Synthesis: A New Challenge[J]. Machine Intelligence Research,2022,19(4):257-287.
APA Deng-Ping Fan,Ziling Huang,Peng Zheng,Hong Liu,Xuebin Qin,&Luc Van Gool.(2022).Facial-sketch Synthesis: A New Challenge.Machine Intelligence Research,19(4),257-287.
MLA Deng-Ping Fan,et al."Facial-sketch Synthesis: A New Challenge".Machine Intelligence Research 19.4(2022):257-287.
Files in This Item: Download All
File Name/Size DocType Version Access License
MIR-2022-03-100.pdf(12340KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Deng-Ping Fan]'s Articles
[Ziling Huang]'s Articles
[Peng Zheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Deng-Ping Fan]'s Articles
[Ziling Huang]'s Articles
[Peng Zheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Deng-Ping Fan]'s Articles
[Ziling Huang]'s Articles
[Peng Zheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: MIR-2022-03-100.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.