A Lightweight Network with Multi-Stage Feature Fusion Module for Single-View 3d Face Reconstruction

Abstract

3D face reconstruction has attracted great attentions of researchers from both academic and industry for its potential application in many scenarios such as face alignment and recognition across large poses. 3D Morphable Model which reconstructs a 3D face through basis coefficients prediction, is usually adopted as the typical parametric framework for 3D face and is suitable to combine with deep learning. Existing cascade regression method predicts coefficients by multiple iterations, which is time-consuming. In this paper, we propose an efficient and end-to-end method for single-view 3D face reconstruction. We build a lightweight network based on mobile blocks with faster speed for parameter extraction and smaller model size. Especially, a multi-stage feature fusion module is designed for enhancing the end-to-end learning. To match the setting of input image size, we updated the pose label of images under various sizes in training dataset before training. Extensive experiments on challenging datasets validate the efficiency of our method for both 3D face reconstruction and face alignment.