We present PhoMoH, a neural network methodology to construct generative models of photorealistic 3D geometry and appearance of human heads including hair, beards, clothing and accessories. 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 photorealistic human head models from relatively little data. The learned generative geometry and appearance networks can be sampled individually and allow the creation of diverse and realistic human heads. Extensive experiments validate our method qualitatively and across different metrics.

翻译：我们介绍了PhoMoH, 这是一种神经网络方法,用来构建光现实的三维几何学和人头外观的基因模型,包括毛发、胡子、服装和配件。与先前的工作不同,PhoMoH用神经场模型人头,从而支持复杂的地形学。我们建议不从零开始学习头型模型,而是增加现有具有新特征的表层头型模型。具体地说,我们学习了一个非常详细的几何网络,在中分辨率头型模型的顶部,加上一个详细、地方几何觉和分解的颜色字段。我们拟议的结构使我们能够从相对较少的数据中学习光现实的人类头型模型。所学的基因几何学和外观网络可以单独抽样,并能够创造多样化和现实的人类头型。广泛的实验验证了我们的方法的质量和跨度。