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We propose a straightforward method that simultaneously reconstructs the 3Dfacial structure and provides dense alignment. To achieve this, we design a 2Drepresentation called UV position map which records the 3D shape of a completeface in UV space, then train a simple Convolutional Neural Network to regressit from a single 2D image. We also integrate a weight mask into the lossfunction during training to improve the performance of the network. Our methoddoes not rely on any prior face model, and can reconstruct full facial geometryalong with semantic meaning. Meanwhile, our network is very light-weighted andspends only 9.8ms to process an image, which is extremely faster than previousworks. Experiments on multiple challenging datasets show that our methodsurpasses other state-of-the-art methods on both reconstruction and alignmenttasks by a large margin.

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network