In this work, we propose an ID-preserving talking head generation framework,
which advances previous methods in two aspects. First, as opposed to
interpolating from sparse flow, we claim that dense landmarks are crucial to
achieving accurate geometry-aware flow fields. Second, inspired by
face-swapping methods, we adaptively fuse the source identity during synthesis,
so that the network better preserves the key characteristics of the image
portrait. Although the proposed model surpasses prior generation fidelity on
established benchmarks, to further make the talking head generation qualified
for real usage, personalized fine-tuning is usually needed. However, this
process is rather computationally demanding that is unaffordable to standard
users. To solve this, we propose a fast adaptation model using a meta-learning
approach. The learned model can be adapted to a high-quality personalized model
as fast as 30 seconds. Last but not the least, a spatial-temporal enhancement
module is proposed to improve the fine details while ensuring temporal
coherency. Extensive experiments prove the significant superiority of our
approach over the state of the arts in both one-shot and personalized settings.

本文提出了一个基于 ID 保持的讲话头生成框架，改进了以往的方法，使用密集地标点实现精确的几何感知流场与自适应融合源身份，使用元学习方法实现快速个性化模型的快速适应，并且增加时空增强模块以提高细节和时序性，实验表明本文的方法在单次和个性化设置中均显著优于现有技术。