  Recent 3D face reconstruction methods reconstruct the entire head compared to
earlier approaches which only model the face. Although these methods accurately
reconstruct facial features, they do not explicitly regulate the upper part of
the head. Extracting information about this part of the head is challenging due
to varying degrees of occlusion by hair. We present a novel approach for
modeling the upper head by removing occluding hair and reconstructing the skin,
revealing information about the head shape. We introduce three objectives: 1) a
dice consistency loss that enforces similarity between the overall head shape
of the source and rendered image, 2) a scale consistency loss to ensure that
head shape is accurately reproduced even if the upper part of the head is not
visible, and 3) a 71 landmark detector trained using a moving average loss
function to detect additional landmarks on the head. These objectives are used
to train an encoder in an unsupervised manner to regress FLAME parameters from
in-the-wild input images. Our unsupervised 3DMM model achieves state-of-the-art
results on popular benchmarks and can be used to infer the head shape, facial
features, and textures for direct use in animation or avatar creation.
