Facelet-Bank for Fast Portrait Manipulation

Abstract

Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks. With a wide variety of user preferences, facial expressions, and accessories, a general and flexible model is necessary to accommodate different types of facial editing. In this paper, we propose a model to achieve this goal based on an end-to-end convolutional neural network that supports fast inference, edit-effect control, and quick partial-model update. In addition, this model learns from unpaired image sets with different attributes. Experimental results show that our framework can handle a wide range of expressions, accessories, and makeup effects. It produces high-resolution and high-quality results in fast speed.

arXiv commentAccepted by CVPR 2018. Code is available on https://github.com/yingcong/Facelet_Bank

References in corpus (12)

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Cited by in corpus (5)

• Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features
• One-shot Face Reenactment
• Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
• PortraitGAN for Flexible Portrait Manipulation
• CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing