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.

翻译：我们提出了一种基于混合神经场的完整人类头部三维形变模型。该模型的核心是一种神经参数化表示，能够在不相交的潜空间中解耦身份特征与面部表情。为此，我们在规范空间中将个体身份特征捕捉为符号距离场（SDF），并通过神经变形场建模面部表情。此外，通过引入围绕面部锚点分布的局部场集成，我们的表示可实现高保真度的局部细节。为促进泛化能力，我们采用定制的高端三维扫描设备，基于新采集的包含255种不同身份的5200余张头部扫描数据集训练模型。无论从几何质量还是完整性角度，该数据集均显著超越现有同类数据集，每张扫描平均包含约350万个网格面。最后，我们证明该方法在拟合误差与重建质量方面均优于当前最优方法。