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

翻译：我们提出PhoMoH，一种利用神经场构建包含头发、胡须、口腔及衣物的照片级真实感人头几何与外观生成模型的神经网络方法。与以往工作不同，PhoMoH采用神经场对人头进行建模，从而支持复杂拓扑结构。我们并未从头学习头部模型，而是提出在现有高表现力头部模型基础上增强新特征：具体而言，我们在中分辨率头部模型之上学习高精细度几何网络，并结合精细的、局部几何感知且解耦的颜色场。所提出的架构能够从较少量数据中学习照片级真实感的人头模型。可独立采样的生成式几何与外观网络能够创建多样且逼真的人头模型。大量实验在定性及多种度量指标上验证了本方法的有效性。