  This paper aims to conduct a comprehensive study on facial-sketch synthesis
(FSS). However, due to the high costs of obtaining hand-drawn sketch datasets,
there lacks 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 2,104 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 classical methods, including 25 handcrafted feature-based facial-sketch
synthesis approaches, 29 general translation methods, and 35 image-to-sketch
approaches. Besides, 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., facial-aware masking and
style-vector expansion, 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.
