Talking-head video editing aims to efficiently insert, delete, and substitute the word of a pre-recorded video through a text transcript editor. The key challenge for this task is obtaining an editing model that generates new talking-head video clips which simultaneously have accurate lip synchronization and motion smoothness. Previous approaches, including 3DMM-based (3D Morphable Model) methods and NeRF-based (Neural Radiance Field) methods, are sub-optimal in that they either require minutes of source videos and days of training time or lack the disentangled control of verbal (e.g., lip motion) and non-verbal (e.g., head pose and expression) representations for video clip insertion. In this work, we fully utilize the video context to design a novel framework for talking-head video editing, which achieves efficiency, disentangled motion control, and sequential smoothness. Specifically, we decompose this framework to motion prediction and motion-conditioned rendering: (1) We first design an animation prediction module that efficiently obtains smooth and lip-sync motion sequences conditioned on the driven speech. This module adopts a non-autoregressive network to obtain context prior and improve the prediction efficiency, and it learns a speech-animation mapping prior with better generalization to novel speech from a multi-identity video dataset. (2) We then introduce a neural rendering module to synthesize the photo-realistic and full-head video frames given the predicted motion sequence. This module adopts a pre-trained head topology and uses only few frames for efficient fine-tuning to obtain a person-specific rendering model. Extensive experiments demonstrate that our method efficiently achieves smoother editing results with higher image quality and lip accuracy using less data than previous methods.

翻译：说话人头视频编辑旨在通过文本转录编辑器高效地插入、删除和替换预录视频中的词语。该任务的关键挑战在于获得一种编辑模型，能够生成同时具备准确唇部同步和运动平滑性的新说话人头视频片段。先前的方法，包括基于3DMM（三维可变形模型）的方法和基于NeRF（神经辐射场）的方法，均非最优方案，因为它们要么需要数分钟源视频和数天训练时间，要么缺乏对视频片段插入中言语表示（如唇部运动）和非言语表示（如头部姿态和表情）的解耦控制。在本工作中，我们充分利用视频上下文设计了一种新颖的说话人头视频编辑框架，实现了高效性、解耦运动控制和时序平滑性。具体而言，我们将该框架分解为运动预测和运动条件渲染两部分：（1）首先设计了一个动画预测模块，该模块能在驱动语音条件下高效获得平滑且唇部同步的运动序列。该模块采用非自回归网络获取上下文先验并提升预测效率，同时从多身份视频数据集中学习具有更好泛化能力的语音-动画映射先验，以应对新颖语音。（2）随后引入神经渲染模块，根据预测的运动序列合成逼真的全头视频帧。该模块采用预训练头部拓扑，仅需少量帧即可高效微调，从而获得特定人物的渲染模型。大量实验表明，与先前方法相比，我们的方法能以更少数据高效实现更平滑的编辑结果，且图像质量和唇部准确度更高。