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

本文介绍了Rig3DGS，通过使用一组三维高斯分布在规范空间中表示整个场景，借助可以学习的形变方法来控制面部表情、头部姿势和视角合成，从而实现从单视角捕获生成高质量渲染的目标。

利用非正式单眼视频创建可控制的人像