  3D face reconstruction plays a very important role in many real-world
multimedia applications, including digital entertainment, social media,
affection analysis, and person identification. The de-facto pipeline for
estimating the parametric face model from an image requires to firstly detect
the facial regions with landmarks, and then crop each face to feed the deep
learning-based regressor. Comparing to the conventional methods performing
forward inference for each detected instance independently, we suggest an
effective end-to-end framework for multi-face 3D reconstruction, which is able
to predict the model parameters of multiple instances simultaneously using
single network inference. Our proposed approach not only greatly reduces the
computational redundancy in feature extraction but also makes the deployment
procedure much easier using the single network model. More importantly, we
employ the same global camera model for the reconstructed faces in each image,
which makes it possible to recover the relative head positions and orientations
in the 3D scene. We have conducted extensive experiments to evaluate our
proposed approach on the sparse and dense face alignment tasks. The
experimental results indicate that our proposed approach is very promising on
face alignment tasks without fully-supervision and pre-processing like
detection and crop. Our implementation is publicly available at
\url{https://github.com/kalyo-zjl/WM3DR}.
