  We present a novel method to jointly learn a 3D face parametric model and 3D
face reconstruction from diverse sources. Previous methods usually learn 3D
face modeling from one kind of source, such as scanned data or in-the-wild
images. Although 3D scanned data contain accurate geometric information of face
shapes, the capture system is expensive and such datasets usually contain a
small number of subjects. On the other hand, in-the-wild face images are easily
obtained and there are a large number of facial images. However, facial images
do not contain explicit geometric information. In this paper, we propose a
method to learn a unified face model from diverse sources. Besides scanned face
data and face images, we also utilize a large number of RGB-D images captured
with an iPhone X to bridge the gap between the two sources. Experimental
results demonstrate that with training data from more sources, we can learn a
more powerful face model.
