HeadGAN: One-shot Neural Head Synthesis and Editing

Understand

Recent attempts to solve the problem of head reenactment using a single reference image have shown promising results.

• However, most of them either perform poorly in terms of photo-realism, or fail to meet the identity preservation problem, or do not fully transfer the driving pose and expression.
• We propose HeadGAN, a novel system that conditions synthesis on 3D face representations, which can be extracted from any driving video and adapted to the facial geometry of any reference image, disentangling identity from expression.
• We further improve mouth movements, by utilising audio features as a complementary input.