  Animation techniques bring digital 3D worlds and characters to life. However,
manual animation is tedious and automated techniques are often specialized to
narrow shape classes. In our work, we propose a technique for automatic
re-animation of arbitrary 3D shapes based on a motion prior extracted from a
video diffusion model. Unlike existing 4D generation methods, we focus solely
on the motion, and we leverage an explicit mesh-based representation compatible
with existing computer-graphics pipelines. Furthermore, our utilization of
diffusion features enhances accuracy of our motion fitting. We analyze efficacy
of these features for animation fitting and we experimentally validate our
approach for two different diffusion models and four animation models. Finally,
we demonstrate that our time-efficient zero-shot method achieves a superior
performance re-animating a diverse set of 3D shapes when compared to existing
techniques in a user study. The project website is located at
https://lukas.uzolas.com/MotionDreamer.
