Facial-Sketch Synthesis: A New Challenge

TL;DR

This paper introduces a new high-quality dataset for facial-sketch synthesis, reviews existing methods, and proposes a simple yet effective baseline model that outperforms previous approaches, advancing FSS research.

Contribution

The paper provides the first comprehensive benchmark for FSS, introduces the FS2K dataset, and proposes the FSGAN model that significantly improves synthesis performance.

Findings

FS2K dataset enhances diversity and scalability for FSS research

FSGAN outperforms all previous models on FS2K dataset

Comprehensive review of 89 classical FSS methods

Abstract

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.