title: RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE
    Optimization on Diffusion Transformers
summary: We propose RoPECraft, a training-free video motion transfer method for
  diffusion transformers that operates solely by modifying their rotary
  positional embeddings (RoPE). We first extract dense optical flow from a
  reference video, and utilize the resulting motion offsets to warp the
  complex-exponential tensors of RoPE, effectively encoding motion into the
  generation process. These embeddings are then further optimized during
  denoising time steps via trajectory alignment between the predicted and target
  velocities using a flow-matching objective. To keep the output faithful to the
  text prompt and prevent duplicate generations, we incorporate a regularization
  term based on the phase components of the reference video's Fourier transform,
  projecting the phase angles onto a smooth manifold to suppress high-frequency
  artifacts. Experiments on benchmarks reveal that RoPECraft outperforms all
  recently published methods, both qualitatively and quantitatively.
paper_authors._id: 682c4205a3bf07bcf1137bed
paper_authors.name: Ahmet Berke Gokmen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-20T19:29:29.622Z
paper_authors._id: 662fcfeddeb646171f1df312
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/V1WdIbDyd20COpAAF70qf.jpeg
paper_authors.fullname: Berke Gökmen
paper_authors.type: user
paper_authors.user: berkegokmen1
paper_authors._id: 682c4205a3bf07bcf1137bee
paper_authors.name: Yigit Ekin
paper_authors._id: 682c4205a3bf07bcf1137bef
paper_authors.name: Bahri Batuhan Bilecen
paper_authors._id: 682c4205a3bf07bcf1137bf0
paper_authors.name: Aysegul Dundar
paper_title: RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE
    Optimization on Diffusion Transformers
paper_summary: We propose RoPECraft, a training-free video motion transfer method for
  diffusion transformers that operates solely by modifying their rotary
  positional embeddings (RoPE). We first extract dense optical flow from a
  reference video, and utilize the resulting motion offsets to warp the
  complex-exponential tensors of RoPE, effectively encoding motion into the
  generation process. These embeddings are then further optimized during
  denoising time steps via trajectory alignment between the predicted and target
  velocities using a flow-matching objective. To keep the output faithful to the
  text prompt and prevent duplicate generations, we incorporate a regularization
  term based on the phase components of the reference video's Fourier transform,
  projecting the phase angles onto a smooth manifold to suppress high-frequency
  artifacts. Experiments on benchmarks reveal that RoPECraft outperforms all
  recently published methods, both qualitatively and quantitatively.
paper_ai_summary: RoPECraft is a training-free method that modifies rotary positional embeddings in diffusion transformers to transfer motion from reference videos, enhancing text-guided video generation and reducing artifacts.
paper_ai_keywords: RoPECraft diffusion transformers rotary positional embeddings dense optical flow complex-exponential tensors denoising time steps trajectory alignment flow-matching objective Fourier transform phase components smooth manifold high-frequency artifacts