Controlling Motion Transfer in Diffusion Transformers via Attention Heads
arXiv:2607.11081
The paper studies how individual attention heads in diffusion transformer models handle motion and spatial structure, and introduces a head-aware method to control motion transfer in video generation without updating model parameters.
Abstract
Diffusion Transformers (DiTs) have advanced video generation with high-quality, temporally coherent results. However, extending them to motion transfer, which requires following reference motion while aligning with a target prompt, remains challenging due to limited understanding of motion and structure representations within DiTs. We analyze video DiTs at the attention-head level and identify distinct heads specialized for motion and spatial structure. Based on this insight, we propose a head-aware controllable motion transfer framework that requires no parameter updates. Our method refines motion cues from motion-specialized heads via semantic correspondence guidance and preserves structure through selective feature injection. This head-level control not only enables accurate motion transfer but also provides an interpretable foundation for controllable video generation with DiTs.
Accepted to ECCV 2026, Project page: https://sunyj-hxppy.github.io/halo/