Towards Rich-Detail 3D Face Reconstruction and Dense Alignment via Multi-Scale Detail Augmentation

3D face reconstruction based on a single image is a longstanding challenging problem in computer vision. Existing end-to-end methods are difficult to reconstruct rich 3D face details. To solve this problem, we propose a two-stream convolutional neural network combined with a face super-resolution method, which can effectively restore the image’s 3D position information. Our method combines an attention fusion mechanism, which can learn the individual attention mapping of each feature subspace, and effectively learn cross-channel information while learning multi-scale and multi-frequency features. Meanwhile, our module obtains the most discriminative features in different local areas, and enhances the consistency and correlation between the attention areas. Experimental results show that our SRCNet has made significant improvements in the 3D face reconstruction and face alignment of the AFLW2000-3D and AFLW datasets.