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.

该研究介绍了 FAGhead 方法，通过使用单目视频，基于传统的三维可塑网格形状和优化中性三维高斯模型，结合可学习的高斯点位置的基于点的可学习表示场（PLRF），以及通过引入 alpha 渲染来管理化身的边缘，实现了高保真度的三维人头像重建和完全控制虚拟图像的表情和姿态，超越了现有方法的性能。