  We present a novel semantic model for human head defined with neural radiance
field. The 3D-consistent head model consist of a set of disentangled and
interpretable bases, and can be driven by low-dimensional expression
coefficients. Thanks to the powerful representation ability of neural radiance
field, the constructed model can represent complex facial attributes including
hair, wearings, which can not be represented by traditional mesh blendshape. To
construct the personalized semantic facial model, we propose to define the
bases as several multi-level voxel fields. With a short monocular RGB video as
input, our method can construct the subject's semantic facial NeRF model with
only ten to twenty minutes, and can render a photo-realistic human head image
in tens of miliseconds with a given expression coefficient and view direction.
With this novel representation, we apply it to many tasks like facial
retargeting and expression editing. Experimental results demonstrate its strong
representation ability and training/inference speed. Demo videos and released
code are provided in our project page:
https://ustc3dv.github.io/NeRFBlendShape/
