Facelet-Bank for Fast Portrait Manipulation
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Digital face manipulation has become a popular and fascinating way to touch images due to the prevalence of smart phones 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 such a model 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 face expressions, accessories, and makeup effects, producing high-resolution and high-quality results in high speed.
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