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.

翻译：三维人体头像的高保真重建在视觉现实领域具有广泛的应用。本文提出FAGhead方法，能够从单目视频中实现完全可控的人像建模。我们显式化传统三维可变形网格（3DMM），并通过优化中性三维高斯分布来重建复杂表情。此外，我们采用具有可学习高斯点位置的新型基于点的可学习表示场（PLRF）来提升重建性能。同时，为有效管理头像边缘，我们引入Alpha渲染技术来监督每个像素的Alpha值。在开源数据集及自采集数据集上的大量实验结果表明，本方法能够生成高保真三维头部头像，并完全控制虚拟头像的表情与姿态，其性能优于现有方法。