Video Motion Transfer with Diffusion Transformers
摘要
We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process the reference video with a pre-trained DiT to analyze cross-frame attention maps and extract a patch-wise motion signal called the Attention Motion Flow (AMF). We guide the latent denoising process in an optimization-based, training-free, manner by optimizing latents with our AMF loss to generate videos reproducing the motion of the reference one. We also apply our optimization strategy to transformer positional embeddings, granting us a boost in zero-shot motion transfer capabilities. We evaluate DiTFlow against recently published methods, outperforming all across multiple metrics and human evaluation.
引用
@article{arxiv.2412.07776,
title = {Video Motion Transfer with Diffusion Transformers},
author = {Alexander Pondaven and Aliaksandr Siarohin and Sergey Tulyakov and Philip Torr and Fabio Pizzati},
journal= {arXiv preprint arXiv:2412.07776},
year = {2025}
}
备注
CVPR 2025 - Project page: https://ditflow.github.io/