  Digital humans and, especially, 3D facial avatars have raised a lot of
attention in the past years, as they are the backbone of several applications
like immersive telepresence in AR or VR. Despite the progress, facial avatars
reconstructed from commodity hardware are incomplete and miss out on parts of
the side and back of the head, severely limiting the usability of the avatar.
This limitation in prior work stems from their requirement of face tracking,
which fails for profile and back views. To address this issue, we propose to
learn person-specific animatable avatars from images without assuming to have
access to precise facial expression tracking. At the core of our method, we
leverage a 3D-aware generative model that is trained to reproduce the
distribution of facial expressions from the training data. To train this
appearance model, we only assume to have a collection of 2D images with the
corresponding camera parameters. For controlling the model, we learn a mapping
from 3DMM facial expression parameters to the latent space of the generative
model. This mapping can be learned by sampling the latent space of the
appearance model and reconstructing the facial parameters from a normalized
frontal view, where facial expression estimation performs well. With this
scheme, we decouple 3D appearance reconstruction and animation control to
achieve high fidelity in image synthesis. In a series of experiments, we
compare our proposed technique to state-of-the-art monocular methods and show
superior quality while not requiring expression tracking of the training data.
