  We present a novel one-shot talking head synthesis method that achieves
disentangled and fine-grained control over lip motion, eye gaze&blink, head
pose, and emotional expression. We represent different motions via disentangled
latent representations and leverage an image generator to synthesize talking
heads from them. To effectively disentangle each motion factor, we propose a
progressive disentangled representation learning strategy by separating the
factors in a coarse-to-fine manner, where we first extract unified motion
feature from the driving signal, and then isolate each fine-grained motion from
the unified feature. We introduce motion-specific contrastive learning and
regressing for non-emotional motions, and feature-level decorrelation and
self-reconstruction for emotional expression, to fully utilize the inherent
properties of each motion factor in unstructured video data to achieve
disentanglement. Experiments show that our method provides high quality
speech&lip-motion synchronization along with precise and disentangled control
over multiple extra facial motions, which can hardly be achieved by previous
methods.
