  Our project proposes an end-to-end 3D face alignment and reconstruction
network. The backbone of our model is built by Bottle-Neck structure via
Depth-wise Separable Convolution. We integrate Coordinate Attention mechanism
and Spatial Group-wise Enhancement to extract more representative features. For
more stable training process and better convergence, we jointly use Wing loss
and the Weighted Parameter Distance Cost to learn parameters for 3D Morphable
model and 3D vertices. Our proposed model outperforms all baseline models both
quantitatively and qualitatively.
