Current advances in human head modeling allow to generate plausible-looking
3D head models via neural representations. Nevertheless, constructing complete
high-fidelity head models with explicitly controlled animation remains an
issue. Furthermore, completing the head geometry based on a partial
observation, e.g. coming from a depth sensor, while preserving details is often
problematic for the existing methods. We introduce a generative model for
detailed 3D head meshes on top of an articulated 3DMM which allows explicit
animation and high-detail preservation at the same time. Our method is trained
in two stages. First, we register a parametric head model with vertex
displacements to each mesh of the recently introduced NPHM dataset of accurate
3D head scans. The estimated displacements are baked into a hand-crafted UV
layout. Second, we train a StyleGAN model in order to generalize over the UV
maps of displacements. The decomposition of the parametric model and
high-quality vertex displacements allows us to animate the model and modify it
semantically. We demonstrate the results of unconditional generation and
fitting to the full or partial observation. The project page is available at
this https URL

通过神经表征，当前人类头部建模的进展允许生成逼真的 3D 头部模型。然而，构建完整的高保真度头部模型，并在明确控制动画的同时保留细节仍然是一个问题。我们引入了一种生成模型，用于在可显式动画和高细节保存之间创建详细的 3D 头部网格。