  Existing approaches to animatable NeRF-based head avatars are either built
upon face templates or use the expression coefficients of templates as the
driving signal. Despite the promising progress, their performances are heavily
bound by the expression power and the tracking accuracy of the templates. In
this work, we present LatentAvatar, an expressive neural head avatar driven by
latent expression codes. Such latent expression codes are learned in an
end-to-end and self-supervised manner without templates, enabling our method to
get rid of expression and tracking issues. To achieve this, we leverage a
latent head NeRF to learn the person-specific latent expression codes from a
monocular portrait video, and further design a Y-shaped network to learn the
shared latent expression codes of different subjects for cross-identity
reenactment. By optimizing the photometric reconstruction objectives in NeRF,
the latent expression codes are learned to be 3D-aware while faithfully
capturing the high-frequency detailed expressions. Moreover, by learning a
mapping between the latent expression code learned in shared and
person-specific settings, LatentAvatar is able to perform expressive
reenactment between different subjects. Experimental results show that our
LatentAvatar is able to capture challenging expressions and the subtle movement
of teeth and even eyeballs, which outperforms previous state-of-the-art
solutions in both quantitative and qualitative comparisons. Project page:
https://www.liuyebin.com/latentavatar.
