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While current talking head models are capable of generating photorealistictalking head videos, they provide limited pose controllability. Most methodsrequire specific video sequences that should exactly contain the head posedesired, being far from user-friendly pose control. Three-dimensional morphablemodels (3DMM) offer semantic pose control, but they fail to capture certainexpressions. We present a novel method that utilizes parametric control of headorientation and facial expression over a pre-trained neural-talking head model.To enable this, we introduce a landmark-parameter morphable model (LPMM), whichoffers control over the facial landmark domain through a set of semanticparameters. Using LPMM, it is possible to adjust specific head pose factors,without distorting other facial attributes. The results show our approachprovides intuitive rig-like control over neural talking head models, allowingboth parameter and image-based inputs.

LPMM: Intuitive Pose Control for Neural Talking-Head Model via Landmark-Parameter Morphable Model