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We propose a novel 3D morphable model (3DMM) for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation which disentangles identity and expressions in a disjoint latent space. 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 2000 face scans from120 different identities using a custom high-end 3D scanning set-up. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M faces per scan. Finally, we demonstrate that our approach outperforms state-of-the-art methods by a significant margin in terms of fitting error and reconstruction quality.
