Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos

Understand

We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild.

• The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
• Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism.
• To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry.