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 (Wnet) 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.

Attribute-guided sketch generation / Tang, H.; Chen, X.; Wang, W.; Xu, D.; Corso, J.; Sebe, N.; Yan, Yan. - (2019). ((Intervento presentato al convegno IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019) tenutosi a Lille nel 14 -18 May 2019 [10.1109/FG.2019.8756586].

Attribute-guided sketch generation

H. Tang;W. Wang;D. Xu;N. Sebe;Y. Yan
2019

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 (Wnet) 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.
IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019)
New York
IEEE
978-1-7281-0089-0
Tang, H.; Chen, X.; Wang, W.; Xu, D.; Corso, J.; Sebe, N.; Yan, Yan
Attribute-guided sketch generation / Tang, H.; Chen, X.; Wang, W.; Xu, D.; Corso, J.; Sebe, N.; Yan, Yan. - (2019). ((Intervento presentato al convegno IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019) tenutosi a Lille nel 14 -18 May 2019 [10.1109/FG.2019.8756586].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/250755
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