  Recent methods for synthesizing 3D-aware face images have achieved rapid
development thanks to neural radiance fields, allowing for high quality and
fast inference speed. However, existing solutions for editing facial geometry
and appearance independently usually require retraining and are not optimized
for the recent work of generation, thus tending to lag behind the generation
process. To address these issues, we introduce NeRFFaceEditing, which enables
editing and decoupling geometry and appearance in the pretrained
tri-plane-based neural radiance field while retaining its high quality and fast
inference speed. Our key idea for disentanglement is to use the statistics of
the tri-plane to represent the high-level appearance of its corresponding
facial volume. Moreover, we leverage a generated 3D-continuous semantic mask as
an intermediary for geometry editing. We devise a geometry decoder (whose
output is unchanged when the appearance changes) and an appearance decoder. The
geometry decoder aligns the original facial volume with the semantic mask
volume. We also enhance the disentanglement by explicitly regularizing rendered
images with the same appearance but different geometry to be similar in terms
of color distribution for each facial component separately. Our method allows
users to edit via semantic masks with decoupled control of geometry and
appearance. Both qualitative and quantitative evaluations show the superior
geometry and appearance control abilities of our method compared to existing
and alternative solutions.
