We offer a new perspective on approaching the task of video generation.
Instead of directly synthesizing a sequence of frames, we propose to render a
video by warping one static image with a generative deformation field (GenDeF).
Such a pipeline enjoys three appealing advantages. First, we can sufficiently
reuse a well-trained image generator to synthesize the static image (also
called canonical image), alleviating the difficulty in producing a video and
thereby resulting in better visual quality. Second, we can easily convert a
deformation field to optical flows, making it possible to apply explicit
structural regularizations for motion modeling, leading to temporally
consistent results. Third, the disentanglement between content and motion
allows users to process a synthesized video through processing its
corresponding static image without any tuning, facilitating many applications
like video editing, keypoint tracking, and video segmentation. Both qualitative
and quantitative results on three common video generation benchmarks
demonstrate the superiority of our GenDeF method.

通过使用生成变形场的方法，我们提出了一种全新的视频生成视角，可以有效地利用一张静态图像进行视频合成以提高视觉质量，并且实现对视频的内容和动作的解耦，使用户可以通过处理对应的静态图像来处理合成的视频，从而方便了许多应用如视频编辑、关键点跟踪和视频分割。