Paper Info
Title
Attribute-Guided Sketch Generation
Abstract
Facial attributes are important since they provide a detailed description and determine the visual appearance of human faces. In this paper, we aim at converting a face image to a sketch while simultaneously generating facial attributes. To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Network (ASGAN) which is an end-to-end framework and contains two pairs of generators and discriminators, one of which is used to generate faces with attributes while the other one is employed for image-to-sketch translation. The two generators form a W-shaped network (W-net) and they are trained jointly with a weight-sharing constraint. Additionally, we also propose two novel discriminators, the residual one focusing on attribute generation and the triplex one helping to generate realistic looking sketches. To validate our model, we have created a new large dataset with 8,804 images, named the Attribute Face Photo & Sketch (AFPS) dataset which is the first dataset containing attributes associated to face sketch images. The experimental results demonstrate that the proposed network (i) generates more photo-realistic faces with sharper facial attributes than baselines and (ii) has good generalization capability on different generative tasks.
Year
DOI
Venue
2019
10.1109/FG.2019.8756586
2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
Keywords
DocType
Volume
Attribute Face Photo & Sketch dataset,human faces,face image,image-to-sketch translation,W-shaped network,face sketch images,attribute-guided sketch generation,attribute-guided sketch generative adversarial network,ASGAN,W-net
Conference
abs/1901.09774
ISSN
ISBN
Citations 
2326-5396
978-1-7281-0090-6
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Hao Tang133834.83
Xinya Chen260.76
Wei Wang313114.16
Dan Xu434216.39
Jason J. Corso5373.84
Nicu Sebe67013403.03
Yan Yan769131.13