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.

使用视频驱动的动画模型，通过 LSTM 神经网络将人对应的表情特征转化为个性化的人物动画参数，从而实现了从视频输入中实现独立于个体的动画。