  High-fidelity reconstruction of 3D human avatars has a wild application in
visual reality. In this paper, we introduce FAGhead, a method that enables
fully controllable human portraits from monocular videos. We explicit the
traditional 3D morphable meshes (3DMM) and optimize the neutral 3D Gaussians to
reconstruct with complex expressions. Furthermore, we employ a novel
Point-based Learnable Representation Field (PLRF) with learnable Gaussian point
positions to enhance reconstruction performance. Meanwhile, to effectively
manage the edges of avatars, we introduced the alpha rendering to supervise the
alpha value of each pixel. Extensive experimental results on the open-source
datasets and our capturing datasets demonstrate that our approach is able to
generate high-fidelity 3D head avatars and fully control the expression and
pose of the virtual avatars, which is outperforming than existing works.
