MotionAdapter: Video Motion Transfer via Content-Aware Attention Customization

TL;DR

MotionAdapter introduces a content-aware framework for transferring complex, semantically aligned motion between videos within diffusion models, enhancing quality and flexibility.

Contribution

It proposes a novel disentanglement and customization approach for motion transfer in diffusion-based video generation, improving robustness and semantic alignment.

Findings

Outperforms state-of-the-art in qualitative and quantitative metrics.

Supports complex motion transfer and editing tasks.

Effectively disentangles motion from appearance using attention analysis.

Abstract

Recent advances in diffusion-based text-to-video models, particularly those built on the diffusion transformer architecture, have achieved remarkable progress in generating high-quality and temporally coherent videos. However, transferring complex motions between videos remains challenging. In this work, we present MotionAdapter, a content-aware motion transfer framework that enables robust and semantically aligned motion transfer within DiT-based video diffusion models. Our key insight is that effective motion transfer requires 1) explicit disentanglement of motion from appearance and 2) adaptive customization of motion to target content. MotionAdapter first isolates motion by analyzing cross-frame attention within 3D full-attention modules to extract attention-derived motion fields. To bridge the semantic gap between reference and target videos, we further introduce a DINO-guided motion customization module that rearranges and refines motion fields based on content correspondences. The customized motion field is then used to guide the DiT denoising process, ensuring that the synthesized video inherits the reference motion while preserving target appearance and semantics. Extensive experiments demonstrate that MotionAdapter outperforms state-of-the-art methods in both qualitative and quantitative evaluations. Moreover, MotionAdapter naturely support complex motion transfer and motion editing tasks such as zooming in/out and composition.