TY - GEN
T1 - Attribute-guided sketch generation
AU - Tang, Hao
AU - Chen, Xinya
AU - Wang, Wei
AU - Xu, Dan
AU - Corso, Jason J.
AU - Sebe, Nicu
AU - Yan, Yan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070457900&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070457900&partnerID=8YFLogxK
U2 - 10.1109/FG.2019.8756586
DO - 10.1109/FG.2019.8756586
M3 - Conference contribution
AN - SCOPUS:85070457900
T3 - Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
BT - Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
T2 - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Y2 - 14 May 2019 through 18 May 2019
ER -