Face Sketch Synthesis in the Wild via Deep Patch Representation-Based Probabilistic Graphical Model

2020 ◽  
Vol 15 ◽  
pp. 172-183 ◽  
Author(s):  
Chunlei Peng ◽  
Nannan Wang ◽  
Jie Li ◽  
Xinbo Gao
Author(s):  
Shengchuan Zhang ◽  
Rongrong Ji ◽  
Jie Hu ◽  
Yue Gao ◽  
Chia-Wen Lin

Despite the extensive progress in face sketch synthesis, existing methods are mostly workable under constrained conditions, such as fixed illumination, pose, background and ethnic origin that are hardly to control in real-world scenarios. The key issue lies in the difficulty to use data under fixed conditions to train a model against imaging variations. In this paper, we propose a novel generative adversarial network termed pGAN, which can generate face sketches efficiently using training data under fixed conditions and handle the aforementioned uncontrolled conditions. In pGAN, we embed key photo priors into the process of synthesis and design a parametric sigmoid activation function for compensating illumination variations. Compared to the existing methods, we quantitatively demonstrate that the proposed method can work well on face photos in the wild.


2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG

Author(s):  
Hongbo Bi ◽  
Ziqi Liu ◽  
Lina Yang ◽  
Kang Wang ◽  
Ning Li

2015 ◽  
Vol 43 (1) ◽  
pp. 267-281 ◽  
Author(s):  
Nikita Mishra ◽  
Huazhe Zhang ◽  
John D. Lafferty ◽  
Henry Hoffmann

2021 ◽  
Vol 438 ◽  
pp. 107-121
Author(s):  
Weiguo Wan ◽  
Yong Yang ◽  
Hyo Jong Lee

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