Abstract:The existing 3D face reconstruction models have the problems of high complexity and poor reconstruction accuracy of multiple face poses. For these reasons, we propose a convolutional neural network that can effectively achieve face alignment and reconstruct a 3D face from a single face picture in the case of a variety of face poses. First, we design an encoder-decoder network composed of a DenseNet module and a deconvolution module. The evaluation of image Structural SIMilarity (SSIM) is introduced into the loss function to construct a new loss function. Then, we train the neural network to get a model, which implements face alignment and 3D face reconstruction tasks. Experiments on the ALFW2000-3D dataset show that the proposed network effectively improves the accuracy of face alignment and reconstruction.