Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment
Yuxing Wang, Yawen Lu, Zhihua Xie, Guoyu Lu
Abstract
We address the problem of reconstructing 3D human face from multi-view facial images using Structure-from-Motion (SfM) based on deep neural networks. While recent learning-based monocular view methods have shown impressive results for 3D facial reconstruction, the single-view setting is easily affected by depth ambiguities and poor face pose issues. In this paper, we propose a novel unsupervised 3D face reconstruction architecture by leveraging the multi-view geometry constraints to train accurate face pose and depth maps. Facial images from multiple perspectives of each 3D face model are input to train the network. Multi-view geometry constraints are fused into unsupervised network by establishing loss constraints from spatial and spectral perspectives. To make the trained 3D face have more details, facial landmark detector is explored to acquire massive facial information to constrain face pose and depth estimation. Through minimizing massive landmark displacement distance by bundle adjustment, an accurate 3D face model can be reconstructed. Extensive experiments demonstrate the superiority of our proposed approach over other methods.