Deep generative models have recently demonstrated the ability to synthesize photorealistic images of human faces with novel identities. A key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate images of complete human heads, we propose an additive decoder that generates plausible additional details such as hair. A novel training scheme enforces a pose independent latent space and in consequence, allows learning of a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities allowing fine-grained control over head pose, face shape, and facial expressions, facilitating a broad range of downstream tasks, like sampling novel identities, re-posing, expression transfer, and more.

中文翻译：

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VariTex：变异神经面纹理

深度生成模型最近证明了具有新颖身份的合成人脸逼真的图像的能力。这种技术的广泛应用的主要挑战是对语义上有意义的参数（外观，头部姿势，面部形状和面部表情）进行独立控制。在本文中，我们提出了VariTex-据我们所知，这是第一种学习神经面部纹理的变化性潜在特征空间的方法，该方法允许对新身份进行采样。我们将此生成模型与参数化面部模型相结合，并获得了对头部姿势和面部表情的显式控制。为了生成完整的人头图像，我们提出了一种附加解码器，该附加解码器生成合理的附加细节，例如头发。一种新颖的训练方案可强制执行一个与姿势无关的潜在空间，因此，可以学习潜在代码与姿势条件好的外部区域之间的一对多映射。所产生的方法可以生成新颖身份的几何形状一致的图像，从而可以对头部姿势，面部形状和面部表情进行细粒度控制，从而促进了广泛的下游任务，例如对新颖身份进行采样，重新摆姿势，表情转移等。