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Facial-sketch Synthesis: A New Challenge

Machine Intelligence Research volume 19, pages 257–287 (2022)Cite this article

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 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 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., facial-aware 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.

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Acknowledgements

This work was supported by the Grant-in-Aid for Japan Society for the Promotion of Science Fellows, Japan (No. 21F50377). The authors would like to thank the anonymous reviewers and editors for their helpful comments on this manuscript. We would like to thank Ning Li from NEPU, China for his help in collecting data and Professor Paul L. Rosin from Cardiff University, UK for the insightful feedback.

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  1. These authors contribute equally to this work

Authors and Affiliations

  1. Computer Vision Laboratory, ETH Zürich, Zürich, 8092, Switzerland

    Deng-Ping Fan & Luc Van Gool

  2. Information and Communication Engineering, University of Tokyo, Tokyo, 113-8654, Japan

    Ziling Huang

  3. Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Peng Zheng & Xuebin Qin

  4. Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, 101-8430, Japan

    Hong Liu

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  1. Deng-Ping Fan
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  3. Peng Zheng
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  4. Hong Liu
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Correspondence to Hong Liu or Xuebin Qin.

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The authors declare that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Colored figures are available in the online version at https://link.springer.com/journal/11633

Deng-Ping Fan received the Ph.D. degree from Nankai University, China in 2019. He joined the Inception Institute of Artificial Intelligence (IIAI), UAE in 2019. He is a Postdoctoral Researcher, working with Prof. Luc Van Gool in Computer Vision Laboratory, ETH Zürich, Switzerland. He has published approximately 50 top journal and conference papers such as TPAMI, CVPR, ICCV, ECCV, etc. He won the Best Paper Finalist Award at IEEE CVPR 2019, and the Best Paper Award Nominee at IEEE CVPR 2020. He was recognized as the CVPR 2019 outstanding reviewer with a special mention award, the CVPR 2020 outstanding reviewer, the ECCV 2020 high-quality reviewer, and the CVPR 2021 outstanding reviewer. He served as a program committee board (PCB) member of IJCAI 2022–2024, a senior program committee (SPC) member of IJCAI 2021, a committee member of China Society of Image and Graphics (CSIG), area chair in NeurIPS 2021 Datasets and Benchmarks Track, area chair in MICCAI2020 Wshp (OMIA7), editorial board member of Computer Vision & AI.

His research interests include computer vision, deep learning, and visual attention, especially the human vision on co-salient object detection, RGB salient object detection, RGB-D salient object detection, and video salient object detection.

Ziling Huang received the B. Sc. degree in electrical engineering from North China Electric Power University, China in 2015, and the M. Sc. degree in electrical engineering from Taiwan Tsing Hua University, Taiwan, China in 2020. She is currently a Ph. D. degree candidate at Department of Information and Communication Engineering, Graduate School of Information Science and Technology, University of Tokyo, Japan. She was an intern student at National Institute of Informatics, Japan in 2019, and at ByteDance, China from 2019 to 2020.

Her research interests include computer vision and machine learning.

Peng Zheng is a master student in visual computing and communication program at Aalto University, Finland and University of Trento, Italy. He was a research intern at Inception Institute of Artificial Intelligence (IIAI),UAE from March 2021 to October 2021. He has been a research assistant in Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), AUE since, January 2022. He serves as the reviewer of IEEE TPAMI.

His research interests include computer vision and machine learning, especially on common information mining and person search.

Hong Liu received the Ph. D. degree from Xiamen University, China in 2020. He is now a Japan Society for the Promotion of Science Fellowship researcher at the National Institute of Informatics, Japan. He has published about 20+ papers in top journals and conferences such as TPAMI, IJCV, TIP, CVPR, ICCV, ECCV, ICLR. He was awarded the Outstanding Doctoral Dissertation Award of the China Society of Image and Graphics, JSPS International Fellowship, and Top-100 Chinese New Stars in Artificial Intelligence by Baidu Scholar.

His research interests include large-scale image retrieval, Riemannian-based machine learning, and adversarial learning.

Xuebin Qin received the Ph. D. degree from University of Alberta, Canada in 2020. Since March 2020, he is a research fellow at Department of Computing Vision, MBZUAI, UAE. He has published about 10 papers in vision and robotics conferences such as CVPR, ECCV, BMVC, ICPR, WACV, IROS.

His research interests include highly accurate image segmentation, salient object detection, image labeling, detection and vision tracking.

Luc Van Gool received the Ph. D. degree in electromechanical engineering at Katholieke Universiteit Leuven, Belgium in 1981. Currently, he is a professor at Katholieke Universiteit Leuven in Belgium and the ETH in Switzerland. He leads computer vision research at both places, and also teaches at both. He has been a program committee member of several major computer vision conferences. He received several Best Paper awards, won a David Marr Prize and a Koenderink Award, and was nominated Distinguished Researcher by the IEEE Computer Science Committee. He is a co-founder of 10 spin-off companies.

His interests include 3D reconstruction and modelling, object recognition, tracking, and gesture analysis, and the combination of those.

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Fan, DP., Huang, Z., Zheng, P. et al. Facial-sketch Synthesis: A New Challenge. Mach. Intell. Res. 19, 257–287 (2022). https://doi.org/10.1007/s11633-022-1349-9

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Keywords

  • Facial sketch synthesis (FSS)
  • facial sketch dataset
  • benchmark
  • attribute
  • style transfer