  We propose a novel 3D morphable model for complete human heads based on
hybrid neural fields. At the core of our model lies a neural parametric
representation that disentangles identity and expressions in disjoint latent
spaces. To this end, we capture a person's identity in a canonical space as a
signed distance field (SDF), and model facial expressions with a neural
deformation field. In addition, our representation achieves high-fidelity local
detail by introducing an ensemble of local fields centered around facial anchor
points. To facilitate generalization, we train our model on a newly-captured
dataset of over 5200 head scans from 255 different identities using a custom
high-end 3D scanning setup. Our dataset significantly exceeds comparable
existing datasets, both with respect to quality and completeness of geometry,
averaging around 3.5M mesh faces per scan. Finally, we demonstrate that our
approach outperforms state-of-the-art methods in terms of fitting error and
reconstruction quality.
