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：我们提出了PhoMoH，一种用于构建包含头发、胡须、口腔及衣物的逼真三维几何与外观生成模型的神经网络方法。与先前工作不同，PhoMoH采用神经场对人头部进行建模，从而支持复杂拓扑结构。我们并非从头学习头部模型，而是通过新增特征增强现有表现力丰富的头部模型。具体而言，我们在中等分辨率头部模型之上学习高细节几何网络，同时结合精细的局部几何感知解耦颜色场。所提出的架构使我们能够从相对少量的数据中学习逼真的人头部模型。学习到的生成式几何与外观网络可单独采样，并支持创建多样化且逼真的头部。大量实验从定性及不同指标维度验证了方法的有效性。