  Recent learning approaches that implicitly represent surface geometry using
coordinate-based neural representations have shown impressive results in the
problem of multi-view 3D reconstruction. The effectiveness of these techniques
is, however, subject to the availability of a large number (several tens) of
input views of the scene, and computationally demanding optimizations. In this
paper, we tackle these limitations for the specific problem of few-shot full 3D
head reconstruction, by endowing coordinate-based representations with a
probabilistic shape prior that enables faster convergence and better
generalization when using few input images (down to three). First, we learn a
shape model of 3D heads from thousands of incomplete raw scans using implicit
representations. At test time, we jointly overfit two coordinate-based neural
networks to the scene, one modeling the geometry and another estimating the
surface radiance, using implicit differentiable rendering. We devise a
two-stage optimization strategy in which the learned prior is used to
initialize and constrain the geometry during an initial optimization phase.
Then, the prior is unfrozen and fine-tuned to the scene. By doing this, we
achieve high-fidelity head reconstructions, including hair and shoulders, and
with a high level of detail that consistently outperforms both state-of-the-art
3D Morphable Models methods in the few-shot scenario, and non-parametric
methods when large sets of views are available.
