  We present MOFA-Video, an advanced controllable image animation method that
generates video from the given image using various additional controllable
signals (such as human landmarks reference, manual trajectories, and another
even provided video) or their combinations. This is different from previous
methods which only can work on a specific motion domain or show weak control
abilities with diffusion prior. To achieve our goal, we design several
domain-aware motion field adapters (\ie, MOFA-Adapters) to control the
generated motions in the video generation pipeline. For MOFA-Adapters, we
consider the temporal motion consistency of the video and generate the dense
motion flow from the given sparse control conditions first, and then, the
multi-scale features of the given image are wrapped as a guided feature for
stable video diffusion generation. We naively train two motion adapters for the
manual trajectories and the human landmarks individually since they both
contain sparse information about the control. After training, the MOFA-Adapters
in different domains can also work together for more controllable video
generation. Project Page: https://myniuuu.github.io/MOFA_Video/
