We present Neural Voice Puppetry, a novel approach for audio-driven facial
video synthesis. Given an audio sequence of a source person or digital
assistant, we generate a photo-realistic output video of a target person that
is in sync with the audio of the source input. This audio-driven facial
reenactment is driven by a deep neural network that employs a latent 3D face
model space. Through the underlying 3D representation, the model inherently
learns temporal stability while we leverage neural rendering to generate
photo-realistic output frames. Our approach generalizes across different
people, allowing us to synthesize videos of a target actor with the voice of
any unknown source actor or even synthetic voices that can be generated
utilizing standard text-to-speech approaches. Neural Voice Puppetry has a
variety of use-cases, including audio-driven video avatars, video dubbing, and
text-driven video synthesis of a talking head. We demonstrate the capabilities
of our method in a series of audio- and text-based puppetry examples, including
comparisons to state-of-the-art techniques and a user study.

本文介绍了神经语音操纵 (Neural Voice Puppetry) 的新方法，即通过深度神经网络驱动的三维面部模型，实现了基于音频和文本的视频合成，包括生成音频驱动的虚拟形象和文本驱动的谈话头像等多种用途。