  Advances in deep neural networks have considerably improved the art of
animating a still image without operating in 3D domain. Whereas, prior arts can
only animate small images (typically no larger than 512x512) due to memory
limitations, difficulty of training and lack of high-resolution (HD) training
datasets, which significantly reduce their potential for applications in movie
production and interactive systems. Motivated by the idea that HD images can be
generated by adding high-frequency residuals to low-resolution results produced
by a neural network, we propose a novel framework known as Animating Through
Warping (ATW) to enable efficient animation of HD images.
  Specifically, the proposed framework consists of two modules, a novel
two-stage neural-network generator and a novel post-processing module known as
Animating Through Warping (ATW). It only requires the generator to be trained
on small images and can do inference on an image of any size. During inference,
an HD input image is decomposed into a low-resolution component(128x128) and
its corresponding high-frequency residuals. The generator predicts the
low-resolution result as well as the motion field that warps the input face to
the desired status (e.g., expressions categories or action units). Finally, the
ResWarp module warps the residuals based on the motion field and adding the
warped residuals to generates the final HD results from the naively up-sampled
low-resolution results. Experiments show the effectiveness and efficiency of
our method in generating high-resolution animations. Our proposed framework
successfully animates a 4K facial image, which has never been achieved by prior
neural models. In addition, our method generally guarantee the temporal
coherency of the generated animations. Source codes will be made publicly
available.
