Learning Neural Parametric Head Models. (arXiv:2212.02761v2 [cs.CV] UPDATED)

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 …

anchor arxiv canonical core ensemble fidelity fields head human hybrid identity lies novel parametric person representation space spaces