Face image animation from a single image has achieved remarkable progress.
However, it remains challenging when only sparse landmarks are available as the
driving signal. Given a source face image and a sequence of sparse face
landmarks, our goal is to generate a video of the face imitating the motion of
landmarks. We develop an efficient and effective method for motion transfer
from sparse landmarks to the face image. We then combine global and local
motion estimation in a unified model to faithfully transfer the motion. The
model can learn to segment the moving foreground from the background and
generate not only global motion, such as rotation and translation of the face,
but also subtle local motion such as the gaze change. We further improve face
landmark detection on videos. With temporally better aligned landmark sequences
for training, our method can generate temporally coherent videos with higher
visual quality. Experiments suggest we achieve results comparable to the
state-of-the-art image driven method on the same identity testing and better
results on cross identity testing.

针对单个图像稀疏地标仅作为驱动信号的情况，研究了一种从稀疏地标生成面部运动视频的有效方法，并结合全局和局部运动估计生成了视觉质量更高且时间上更连贯的视频。

面部图像动画的稀疏到稠密的运动转移

Sparse to Dense Motion Transfer for Face Image Animation

We propose a novel end-to-end deep architecture for face landmark detection,
based on a deep convolutional and deconvolutional network followed by carefully
designed recurrent network structures. The pipeline of this architecture
consists of three parts. Through the first part, we encode an input face image
to resolution-preserved deconvolutional feature maps via a deep network with
stacked convolutional and deconvolutional layers. Then, in the second part, we
estimate the initial coordinates of the facial key points by an additional
convolutional layer on top of these deconvolutional feature maps. In the last
part, by using the deconvolutional feature maps and the initial facial key
points as input, we refine the coordinates of the facial key points by a
recurrent network that consists of multiple Long-Short Term Memory (LSTM)
components. Extensive evaluations on several benchmark datasets show that the
proposed deep architecture has superior performance against the
state-of-the-art methods.

提出一种新颖的端到端深度架构用于面部标记检测，它基于卷积和反卷积网络，以及经过精心设计的循环网络结构，并在多个基准数据集上得到了比当前最先进方法更卓越的表现。