Collecting and labeling training data is one important step for
learning-based methods because the process is time-consuming and biased. For
face analysis tasks, although some generative models can be used to generate
face data, they can only achieve a subset of generation diversity,
reconstruction accuracy, 3D consistency, high-fidelity visual quality, and easy
editability. One recent related work is the graphics-based generative method,
but it can only render low realism head with high computation cost. In this
paper, we propose MetaHead, a unified and full-featured controllable digital
head engine, which consists of a controllable head radiance field(MetaHead-F)
to super-realistically generate or reconstruct view-consistent 3D controllable
digital heads and a generic top-down image generation framework LabelHead to
generate digital heads consistent with the given customizable feature labels.
Experiments validate that our controllable digital head engine achieves the
state-of-the-art generation visual quality and reconstruction accuracy.
Moreover, the generated labeled data can assist real training data and
significantly surpass the labeled data generated by graphics-based methods in
terms of training effect.

本文提出了 MetaHead，一个全面的可控数字头部生成引擎，以生成视图一致的 3D 可控数字头部，并生成与给定自定义特征标签一致的数字头部。实验证明，我们的可控数字头部发动机实现了最先进的生成视觉质量和重建准确性，并且生成的标记数据可以协助真实训练数据。