RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE
  Optimization on Diffusion Transformers
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.
682c4205a3bf07bcf1137bed
Ahmet Berke Gokmen
claimed_verified
2025-05-20T19:29:29.622Z
662fcfeddeb646171f1df312
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/V1WdIbDyd20COpAAF70qf.jpeg
Berke Gökmen
user
berkegokmen1
682c4205a3bf07bcf1137bee
Yigit Ekin
682c4205a3bf07bcf1137bef
Bahri Batuhan Bilecen
682c4205a3bf07bcf1137bf0
Aysegul Dundar
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE
  Optimization on Diffusion Transformers
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.
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.
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