Facelet-Bank for Fast Portrait Manipulation
This work addresses the need for general and efficient facial editing tools for users on smartphones and social networks, though it appears incremental in its approach.
The paper tackled the problem of flexible and fast digital face manipulation by proposing an end-to-end convolutional neural network model that learns from unpaired image sets, achieving high-resolution, high-quality results with fast inference speeds.
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.