Giebenhain, Simon; Kirschstein, Tobias; Georgopoulos, Markos; Rünz, Martin; Agapito, Lourdes; Nießner, Matthias; (2023) Learning Neural Parametric Head Models. In: O'Conner, Lisa, (ed.) 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (pp. pp. 21003-21012). IEEE: Vancouver, BC, Canada.

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Abstract

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 3700 head scans from 203 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 1 1 We will publicly release our dataset along with a public benchmark for both neural head avatar construction as well as an evaluation on a hidden test-set for inference-time fitting.. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality.

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