Video-Driven Animation of Neural Head Avatars
Wolfgang Paier Paul Hinzer Anna Hilsmann Peter Eisert
We present a new approach for video-driven animation of high-quality neural
3D head models, addressing the challenge of person-independent animation from
video input. Typically, high-quality generative models are learned for specific
individuals from multi-view video footage, resulting in person-specific latent
representations that drive the generation process. In order to achieve
person-independent animation from video input, we introduce an LSTM-based
animation network capable of translating person-independent expression features
into personalized animation parameters of person-specific 3D head models. Our
approach combines the advantages of personalized head models (high quality and
realism) with the convenience of video-driven animation employing multi-person
facial performance capture. We demonstrate the effectiveness of our approach on
synthesized animations with high quality based on different source videos as
well as an ablation study.