  High-fidelity face digitization solutions often combine multi-view stereo
(MVS) techniques for 3D reconstruction and a non-rigid registration step to
establish dense correspondence across identities and expressions. A common
problem is the need for manual clean-up after the MVS step, as 3D scans are
typically affected by noise and outliers and contain hairy surface regions that
need to be cleaned up by artists. Furthermore, mesh registration tends to fail
for extreme facial expressions. Most learning-based methods use an underlying
3D morphable model (3DMM) to ensure robustness, but this limits the output
accuracy for extreme facial expressions. In addition, the global bottleneck of
regression architectures cannot produce meshes that tightly fit the ground
truth surfaces. We propose ToFu, Topologically consistent Face from multi-view,
a geometry inference framework that can produce topologically consistent meshes
across facial identities and expressions using a volumetric representation
instead of an explicit underlying 3DMM. Our novel progressive mesh generation
network embeds the topological structure of the face in a feature volume,
sampled from geometry-aware local features. A coarse-to-fine architecture
facilitates dense and accurate facial mesh predictions in a consistent mesh
topology. ToFu further captures displacement maps for pore-level geometric
details and facilitates high-quality rendering in the form of albedo and
specular reflectance maps. These high-quality assets are readily usable by
production studios for avatar creation, animation and physically-based skin
rendering. We demonstrate state-of-the-art geometric and correspondence
accuracy, while only taking 0.385 seconds to compute a mesh with 10K vertices,
which is three orders of magnitude faster than traditional techniques. The code
and the model are available for research purposes at
https://tianyeli.github.io/tofu.
