Abstract
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.
Abstract (translated)
我们提出了一种名为RoPECraft的方法，这是一种无需训练的视频运动转移方法，专门针对扩散变换器。该方法仅通过修改其旋转位置嵌入（RoPE）来操作。首先从参考视频中提取密集光流，并利用由此产生的运动偏移量对RoPE的复指数张量进行变形处理，从而将运动编码到生成过程中。在去噪阶段，我们进一步优化这些嵌入，通过预测速度和目标速度之间的轨迹对齐，采用流动匹配的目标函数来完成这一过程。为了确保输出忠于文本提示并防止重复生成，我们在参考视频傅里叶变换的相位成分基础上引入了一项正则化项，将相位角度投影到平滑流形上以抑制高频伪影。 在基准测试中的实验表明，RoPECraft无论是在定性还是定量评估中都优于所有最近发表的方法。
URL
https://arxiv.org/abs/2505.13344
