  We present a new method for text-driven motion transfer - synthesizing a
video that complies with an input text prompt describing the target objects and
scene while maintaining an input video's motion and scene layout. Prior methods
are confined to transferring motion across two subjects within the same or
closely related object categories and are applicable for limited domains (e.g.,
humans). In this work, we consider a significantly more challenging setting in
which the target and source objects differ drastically in shape and
fine-grained motion characteristics (e.g., translating a jumping dog into a
dolphin). To this end, we leverage a pre-trained and fixed text-to-video
diffusion model, which provides us with generative and motion priors. The
pillar of our method is a new space-time feature loss derived directly from the
model. This loss guides the generation process to preserve the overall motion
of the input video while complying with the target object in terms of shape and
fine-grained motion traits.
