While current talking head models are capable of generating photorealistic
talking head videos, they provide limited pose controllability. Most methods
require specific video sequences that should exactly contain the head pose
desired, being far from user-friendly pose control. Three-dimensional morphable
models (3DMM) offer semantic pose control, but they fail to capture certain
expressions. We present a novel method that utilizes parametric control of head
orientation and facial expression over a pre-trained neural-talking head model.
To enable this, we introduce a landmark-parameter morphable model (LPMM), which
offers control over the facial landmark domain through a set of semantic
parameters. Using LPMM, it is possible to adjust specific head pose factors,
without distorting other facial attributes. The results show our approach
provides intuitive rig-like control over neural talking head models, allowing
both parameter and image-based inputs.

本文提出了一种新方法，使用参数化控制头部方向和面部表情，从而在预训练的神经网络对话头模型上提供直观的刚体控制，允许参数和基于图像的输入。