LPMM: Intuitive Pose Control for Neural Talking-Head Model via
  Landmark-Parameter Morphable Model
Kwangho Lee Patrick Kwon Myung Ki Lee Namhyuk Ahn Junsoo Lee
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.