MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with
  Mixture of Score Guidance
Authors: name: Hidir Yesiltepe, name: Tuna Han Salih Meral, name: Connor Dunlop, name: Pinar Yanardag

In this work, we propose the first motion transfer approach in diffusion
transformer through Mixture of Score Guidance (MSG), a theoretically-grounded
framework for motion transfer in diffusion models. Our key theoretical
contribution lies in reformulating conditional score to decompose motion score
and content score in diffusion models. By formulating motion transfer as a
mixture of potential energies, MSG naturally preserves scene composition and
enables creative scene transformations while maintaining the integrity of
transferred motion patterns. This novel sampling operates directly on
pre-trained video diffusion models without additional training or fine-tuning.
Through extensive experiments, MSG demonstrates successful handling of diverse
scenarios including single object, multiple objects, and cross-object motion
transfer as well as complex camera motion transfer. Additionally, we introduce
MotionBench, the first motion transfer dataset consisting of 200 source videos
and 1000 transferred motions, covering single/multi-object transfers, and
complex camera motions.