MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with
  Mixture of Score Guidance
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.
675853a66bb8fc7121d27886
Hidir Yesiltepe
claimed_verified
2024-12-10T15:15:16.912Z
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Hidir Yesiltepe
user
Hidir
675853a66bb8fc7121d27887
Tuna Han Salih Meral
admin_assigned
2024-12-10T15:18:51.292Z
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Tuna Han Salih Meral
user
tmeral
675853a66bb8fc7121d27888
Connor Dunlop
admin_assigned
2024-12-10T16:21:14.722Z
65302b5769e5dbd6a2d4b4ee
/avatars/cac79a5fb051a539ffaa15596974673b.svg
Connor Dunlop
user
cdunlop
675853a66bb8fc7121d27889
Pinar Yanardag
claimed_verified
2025-06-02T07:55:18.038Z
683b9c7d4568ffd33e7334e1
/avatars/046bd5a78a24f3bc4e1f6cb6076970e0.svg
Pinar Yanardag
user
Pinguar
MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with
  Mixture of Score Guidance
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.
MSG, a novel framework based on mixture of score guidance, enables motion transfer in diffusion transformers without fine-tuning, preserving scene composition and handling diverse motion scenarios.
diffusion transformer Mixture of Score Guidance MSG conditional score motion score content score diffusion models potential energies scene composition creative scene transformations pre-trained video diffusion models MotionBench single object multiple objects cross-object motion transfer complex camera motion transfer