  We present PhoMoH, a neural network methodology to construct generative
models of photo-realistic 3D geometry and appearance of human heads including
hair, beards, an oral cavity, and clothing. In contrast to prior work, PhoMoH
models the human head using neural fields, thus supporting complex topology.
Instead of learning a head model from scratch, we propose to augment an
existing expressive head model with new features. Concretely, we learn a highly
detailed geometry network layered on top of a mid-resolution head model
together with a detailed, local geometry-aware, and disentangled color field.
Our proposed architecture allows us to learn photo-realistic human head models
from relatively little data. The learned generative geometry and appearance
networks can be sampled individually and enable the creation of diverse and
realistic human heads. Extensive experiments validate our method qualitatively
and across different metrics.
