  3D Morphable Model (3DMM) fitting has widely benefited face analysis due to
its strong 3D priori. However, previous reconstructed 3D faces suffer from
degraded visual verisimilitude due to the loss of fine-grained geometry, which
is attributed to insufficient ground-truth 3D shapes, unreliable training
strategies and limited representation power of 3DMM. To alleviate this issue,
this paper proposes a complete solution to capture the personalized shape so
that the reconstructed shape looks identical to the corresponding person.
Specifically, given a 2D image as the input, we virtually render the image in
several calibrated views to normalize pose variations while preserving the
original image geometry. A many-to-one hourglass network serves as the
encode-decoder to fuse multiview features and generate vertex displacements as
the fine-grained geometry. Besides, the neural network is trained by directly
optimizing the visual effect, where two 3D shapes are compared by measuring the
similarity between the multiview images rendered from the shapes. Finally, we
propose to generate the ground-truth 3D shapes by registering RGB-D images
followed by pose and shape augmentation, providing sufficient data for network
training. Experiments on several challenging protocols demonstrate the superior
reconstruction accuracy of our proposal on the face shape.
