Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases
Pengrui Wang, Yi Tian, Wujun Che, Bo Xu · 2019
We propose an efficient and accurate regression-based 3D face shape reconstruction method. We use an encoder based on MobileNet to estimate parameters including face pose and coefficients of a parametric face model from a single face image. The encoder is trained only by 2D landmarks. Faces can be reconstructed by these parameters. Three contributions of our method are: 1) we propose a databases fusion method to train our network which can easily utilize multiple 2D landmark databases which have different landmark numbers and positions; 2) with the fusion method, we propose a simple MobileNet based network which is efficient, accurate and robust for face reconstruction even without complex training strategies; 3) we add an additional deformation field for shape correction to further improve our network's performance. Experiments demonstrate our method can bring about great performance improvement on most test databases and also compare favorably to some state-of-the-art methods in performance and speed.