来自今天的爱可可AI前沿推介

[CV] PhoMoH: Implicit Photorealistic 3D Models of Human Heads

M Zanfir, T Alldieck, C Sminchisescu
[Google Research]

PhoMoH: 头部隐式逼真3D模型

要点：

  1. 提出PhoMoH，一种构建逼真3D头部几何和外观模型的神经网络方法；
  2. 用神经场对头部进行建模，支持复杂拓扑结构；
  3. 所提出的几何网络可以从相对少量数据中学习逼真头部模型。

摘要：
本文提出PhoMoH，一种神经网络方法，用于构建人体头部(包括头发、胡须、服装和配件)的逼真3D几何和外观生成模型。与之前的工作不同，PhoMoH使用神经场对头部进行建模，从而支持复杂的拓扑结构。本文没有从头开始学习一个头部模型，而是用新的特征来增强现有的具有表现力的头部模型。在一个中等分辨率的头部模型之上学习一个高度详细的几何网络，同时学习一个详细的、局部几何感知的、解缠的颜色场。所提出的架构使得能从相对较少的数据中学习逼真的人体头部模型。学到的生成式几何和外观网络可以被单独采样，并允许创建多样化和逼真的头部。广泛的实验从质量上和不同的指标上验证了所提出方法。

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.

论文链接：https://arxiv.org/abs/2212.07275
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