  The ability to create realistic, animatable and relightable head avatars from
casual video sequences would open up wide ranging applications in communication
and entertainment. Current methods either build on explicit 3D morphable meshes
(3DMM) or exploit neural implicit representations. The former are limited by
fixed topology, while the latter are non-trivial to deform and inefficient to
render. Furthermore, existing approaches entangle lighting in the color
estimation, thus they are limited in re-rendering the avatar in new
environments. In contrast, we propose PointAvatar, a deformable point-based
representation that disentangles the source color into intrinsic albedo and
normal-dependent shading. We demonstrate that PointAvatar bridges the gap
between existing mesh- and implicit representations, combining high-quality
geometry and appearance with topological flexibility, ease of deformation and
rendering efficiency. We show that our method is able to generate animatable 3D
avatars using monocular videos from multiple sources including hand-held
smartphones, laptop webcams and internet videos, achieving state-of-the-art
quality in challenging cases where previous methods fail, e.g., thin hair
strands, while being significantly more efficient in training than competing
methods.
