title: MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with
    Mixture of Score Guidance
summary: 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.
paper_authors._id: 675853a66bb8fc7121d27886
paper_authors.name: Hidir Yesiltepe
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-10T15:15:16.912Z
paper_authors._id: 63597e84d72fc0539e72b507
paper_authors.avatarUrl: /avatars/f568302b70064220e3c824577e5bece4.svg
paper_authors.fullname: Hidir Yesiltepe
paper_authors.type: user
paper_authors.user: Hidir
paper_authors._id: 675853a66bb8fc7121d27887
paper_authors.name: Tuna Han Salih Meral
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-10T15:18:51.292Z
paper_authors._id: 64f8b03f83807928d25e766f
paper_authors.avatarUrl: /avatars/68fd4ee967a1673a1d78a7581be8b3da.svg
paper_authors.fullname: Tuna Han Salih Meral
paper_authors.type: user
paper_authors.user: tmeral
paper_authors._id: 675853a66bb8fc7121d27888
paper_authors.name: Connor Dunlop
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-10T16:21:14.722Z
paper_authors._id: 65302b5769e5dbd6a2d4b4ee
paper_authors.avatarUrl: /avatars/cac79a5fb051a539ffaa15596974673b.svg
paper_authors.fullname: Connor Dunlop
paper_authors.type: user
paper_authors.user: cdunlop
paper_authors._id: 675853a66bb8fc7121d27889
paper_authors.name: Pinar Yanardag
paper_title: MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with
    Mixture of Score Guidance
paper_summary: 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.
paper_ai_keywords: diffusion transformer Mixture of Score Guidance (MSG) conditional score motion score content score potential energies pre-trained video diffusion models MotionBench