  Creating controllable 3D human portraits from casual smartphone videos is
highly desirable due to their immense value in AR/VR applications. The recent
development of 3D Gaussian Splatting (3DGS) has shown improvements in rendering
quality and training efficiency. However, it still remains a challenge to
accurately model and disentangle head movements and facial expressions from a
single-view capture to achieve high-quality renderings. In this paper, we
introduce Rig3DGS to address this challenge. We represent the entire scene,
including the dynamic subject, using a set of 3D Gaussians in a canonical
space. Using a set of control signals, such as head pose and expressions, we
transform them to the 3D space with learned deformations to generate the
desired rendering. Our key innovation is a carefully designed deformation
method which is guided by a learnable prior derived from a 3D morphable model.
This approach is highly efficient in training and effective in controlling
facial expressions, head positions, and view synthesis across various captures.
We demonstrate the effectiveness of our learned deformation through extensive
quantitative and qualitative experiments. The project page can be found at
http://shahrukhathar.github.io/2024/02/05/Rig3DGS.html
