  We present to recover the complete 3D facial geometry from a single depth
view by proposing an Attention Guided Generative Adversarial Networks (AGGAN).
In contrast to existing work which normally requires two or more depth views to
recover a full 3D facial geometry, the proposed AGGAN is able to generate a
dense 3D voxel grid of the face from a single unconstrained depth view.
Specifically, AGGAN encodes the 3D facial geometry within a voxel space and
utilizes an attention-guided GAN to model the illposed 2.5D depth-3D mapping.
Multiple loss functions, which enforce the 3D facial geometry consistency,
together with a prior distribution of facial surface points in voxel space are
incorporated to guide the training process. Both qualitative and quantitative
comparisons show that AGGAN recovers a more complete and smoother 3D facial
shape, with the capability to handle a much wider range of view angles and
resist to noise in the depth view than conventional methods
