  The ability to animate photo-realistic head avatars reconstructed from
monocular portrait video sequences represents a crucial step in bridging the
gap between the virtual and real worlds. Recent advancements in head avatar
techniques, including explicit 3D morphable meshes (3DMM), point clouds, and
neural implicit representation have been exploited for this ongoing research.
However, 3DMM-based methods are constrained by their fixed topologies,
point-based approaches suffer from a heavy training burden due to the extensive
quantity of points involved, and the last ones suffer from limitations in
deformation flexibility and rendering efficiency. In response to these
challenges, we propose MonoGaussianAvatar (Monocular Gaussian Point-based Head
Avatar), a novel approach that harnesses 3D Gaussian point representation
coupled with a Gaussian deformation field to learn explicit head avatars from
monocular portrait videos. We define our head avatars with Gaussian points
characterized by adaptable shapes, enabling flexible topology. These points
exhibit movement with a Gaussian deformation field in alignment with the target
pose and expression of a person, facilitating efficient deformation.
Additionally, the Gaussian points have controllable shape, size, color, and
opacity combined with Gaussian splatting, allowing for efficient training and
rendering. Experiments demonstrate the superior performance of our method,
which achieves state-of-the-art results among previous methods.
