Knowledge Commons of Institute of Automation，CAS
| Facial-sketch Synthesis: A New Challenge | |
| Deng-Ping Fan1 | |
| Source Publication | Machine Intelligence Research
 |
| ISSN | 2731-538X |
| 2022 | |
| Volume | 19Pages:257-287 |
| Abstract | 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 https://github.com/DengPingFan/FSGAN. |
| Keyword | Facial sketch synthesis (FSS) facial sketch dataset benchmark attribute style transfer |
| DOI | 10.1007/s11633-022-1349-9 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49642 |
| Collection | 学术期刊_Machine Intelligence Research |
| Affiliation | 1.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. Facial-sketch Synthesis: A New Challenge[J]. Machine Intelligence Research,2022,19:257-287. |
| APA | Deng-Ping Fan.(2022).Facial-sketch Synthesis: A New Challenge.Machine Intelligence Research,19,257-287. |
| MLA | Deng-Ping Fan."Facial-sketch Synthesis: A New Challenge".Machine Intelligence Research 19(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-SA | View Download | |
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