This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of ob-taining 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 data-sets 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 gener-al translation methods,and 35 image-to-sketch approaches.In addition,we elaborate comprehensive experiments on the existing 19 cut-ting-edge models.Third,we present a simple baseline for FSS,named FSGAN.With only two straightforward components,i.e.,facial-aware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the pro-posed FS2K dataset by a large margin.Finally,we conclude with lessons learned over the past years and point out several unsolved chal-lenges.Our code is available at .

Deng-Ping Fan;Ziling Huang;Peng Zheng;Hong Liu;Xuebin Qin;Luc Van Gool

Computer Vision Laboratory,ETH Zurich,Zurich 8092,Switzerland;Information and Communication Engineering,University of Tokyo,Tokyo 113-8654,Japan;Computer Vision,Mohamed bin Zayed University of Artificial Intelligence,Abu Dhabi,UAE;Digital Content and Media Sciences Research Division,National Institute of Informatics,Tokyo 101-8430,Japan

机器智能研究（英文）

[1]Deng-Ping Fan;Ziling Huang;Peng Zheng;Hong Liu;Xuebin Qin;Luc Van Gool-.Facial-sketch Synthesis:A New Challenge)[J].机器智能研究（英文）,2022(04):257-287

FS2K,FSGAN

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