  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.
