  Efficiently generating a freestyle 3D portrait with high quality and
3D-consistency is a promising yet challenging task. The portrait styles
generated by most existing methods are usually restricted by their 3D
generators, which are learned in specific facial datasets, such as FFHQ. To get
the diverse 3D portraits, one can build a large-scale multi-style database to
retrain a 3D-aware generator, or use a off-the-shelf tool to do the style
translation. However, the former is time-consuming due to data collection and
training process, the latter may destroy the multi-view consistency. To tackle
this problem, we propose a novel text-driven 3D-aware portrait synthesis
framework that can generate out-of-distribution portrait styles. Specifically,
for a given portrait style prompt, we first composite two generative priors, a
3D-aware GAN generator and a text-guided image editor, to quickly construct a
few-shot stylized portrait set. Then we map the special style domain of this
set to our proposed 3D latent feature generator and obtain a 3D representation
containing the given style information. Finally we use a pre-trained 3D
renderer to generate view-consistent stylized portraits from the 3D
representation. Extensive experimental results show that our method is capable
of synthesizing high-quality 3D portraits with specified styles in a few
minutes, outperforming the state-of-the-art.
