Automated Blendshape Personalization for Faithful Face Animations Using Commodity Smartphones
Timo Menzel, Mario Botsch, Marc Erich Latoschik · 2022
Digital reconstruction of humans has various interesting use-cases. Animated virtual humans, avatars and agents alike, are the central entities in virtual embodied human-computer and human-human encounters in social XR. Here, a faithful reconstruction of facial expressions becomes paramount due to their prominent role in non-verbal behavior and social interaction. Current XR-platforms, like Unity 3D or the Unreal Engine, integrate recent smartphone technologies to animate faces of virtual humans by facial motion capturing. Using the same technology, this article presents an optimization-based approach to generate personalized blendshapes as animation targets for facial expressions. The proposed method combines a position-based optimization with a seamless partial deformation transfer, necessary for a faithful reconstruction. Our method is fully automated and considerably outperforms existing solutions based on example-based facial rigging or deformation transfer, and overall results in a much lower reconstruction error. It also neatly integrates with recent smartphone-based reconstruction pipelines for mesh generation and automated rigging, further paving the way to a widespread application of human-like and personalized avatars and agents in various use-cases.