Improved 3D face reconstruction and expression driving based on ResNest

Abstract

Currently, 3D face modeling has been more and more widely used in medical, animation, games, and other fields. With the application of deep neural network, 3D face modeling method based on deep learning is the main research direction at present. However, the accuracy of the 3D face reconstruction model is generally insufficient. To solve this problem, an improved algorithm model based on deep residual network is proposed in this paper. A new convolutional module of Squeeze-and-Excitation is introduced to increase the channel dependence of the entire model, and then Blendshape is combined to drive the entire face data. The normalization error of the new improved neural network algorithm is only 3.12% compared with other traditional algorithm models. The loss function was reduced by 56.72%, 48.95%, and 41.86%, respectively. Compared with the traditional algorithm, the improved algorithm has higher precision and more perfect expression driving ability. This study contributes to improving the accuracy of the whole 3D face reconstruction technology.