论文标题
Phomoh：人头的隐式感性3D模型
PhoMoH: Implicit Photorealistic 3D Models of Human Heads
论文作者
论文摘要
我们提出了Phomoh，这是一种神经网络方法论，旨在构建照片真实3D几何形状的生成模型和人头外观，包括头发，胡须，口腔和衣服。与先前的工作相反，Phomoh使用神经场对人头进行建模，从而支持复杂的拓扑。我们建议没有从头开始学习头部模型，而是建议增强具有新功能的现有表达式模型。具体而言，我们学习了一个高度详细的几何网络，该网络在中分辨率头模型的顶部以及详细的局部几何学意识和分离的颜色字段以及详细的局部几何形状。我们提出的架构使我们能够从相对较少的数据中学习照片真实的人头模型。可以单独采样学到的生成几何形状和外观网络，并能够创建各种和现实的人头。广泛的实验可以定性地验证我们的方法。
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.
