  DiffusionAvatars synthesizes a high-fidelity 3D head avatar of a person,
offering intuitive control over both pose and expression. We propose a
diffusion-based neural renderer that leverages generic 2D priors to produce
compelling images of faces. For coarse guidance of the expression and head
pose, we render a neural parametric head model (NPHM) from the target
viewpoint, which acts as a proxy geometry of the person. Additionally, to
enhance the modeling of intricate facial expressions, we condition
DiffusionAvatars directly on the expression codes obtained from NPHM via
cross-attention. Finally, to synthesize consistent surface details across
different viewpoints and expressions, we rig learnable spatial features to the
head's surface via TriPlane lookup in NPHM's canonical space. We train
DiffusionAvatars on RGB videos and corresponding fitted NPHM meshes of a person
and test the obtained avatars in both self-reenactment and animation scenarios.
Our experiments demonstrate that DiffusionAvatars generates temporally
consistent and visually appealing videos for novel poses and expressions of a
person, outperforming existing approaches.
