  With NeRF widely used for facial reenactment, recent methods can recover
photo-realistic 3D head avatar from just a monocular video. Unfortunately, the
training process of the NeRF-based methods is quite time-consuming, as MLP used
in the NeRF-based methods is inefficient and requires too many iterations to
converge. To overcome this problem, we propose AvatarMAV, a fast 3D head avatar
reconstruction method using Motion-Aware Neural Voxels. AvatarMAV is the first
to model both the canonical appearance and the decoupled expression motion by
neural voxels for head avatar. In particular, the motion-aware neural voxels is
generated from the weighted concatenation of multiple 4D tensors. The 4D
tensors semantically correspond one-to-one with 3DMM expression basis and share
the same weights as 3DMM expression coefficients. Benefiting from our novel
representation, the proposed AvatarMAV can recover photo-realistic head avatars
in just 5 minutes (implemented with pure PyTorch), which is significantly
faster than the state-of-the-art facial reenactment methods. Project page:
https://www.liuyebin.com/avatarmav.
