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：对话头部视频编辑旨在通过文本转录编辑器，高效地插入、删除和替换预先录制视频中的词语。该任务的关键挑战在于获取一个编辑模型，该模型能生成同时具备精确唇部同步和动作平滑性的新对话头部视频片段。以往的方法，包括基于3D形变模型（3DMM）的方法和基于神经辐射场（NeRF）的方法，均非最优：它们要么需要数分钟的源视频和数天的训练时间，要么在视频片段插入时缺乏对言语（如唇部动作）和非言语（如头部姿态和表情）表示的分离控制。在本工作中，我们充分利用视频上下文设计了一种用于对话头部视频编辑的新框架，该框架实现了高效性、分离动作控制以及序列平滑性。具体而言，我们将该框架分解为动作预测和动作条件渲染两部分：（1）我们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取平滑且唇部同步的动作序列。该模块采用非自回归网络以获得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一个具有更好泛化能力的语音-动画映射先验，以处理新颖语音。（2）随后，我们引入了一个神经渲染模块，该模块根据预测的动作序列合成逼真且完整的头部视频帧。该模块采用预训练的头部拓扑结构，并仅使用少量帧进行高效微调，以获得特定人物的渲染模型。大量实验表明，与以往方法相比，本方法能在使用更少数据的情况下，更高效地实现更平滑的编辑结果，并具有更高的图像质量和唇部准确性。