通过注意力头控制扩散模型，实现精准动作迁移。
Controlling Motion Transfer in Diffusion Transformers via Attention Heads
- 按注意力头区分运动与结构特征，针对性提取动作信息。
- 无需更新参数，仅靠特征重调即可实现高保真动作迁移。
- 方法可解释性强，适合需要精细动作控制的视频生成场景。
扩散变压器（DiTs）在视频生成中取得了高质量、时间连贯的结果。然而，将它们扩展到动作迁移任务仍具挑战性，因为对DiTs内部运动与结构表征的理解有限。本文从注意力头层面分析视频DiTs，发现部分头专门处理运动，另一些头则专注空间结构。基于此，我们提出一种无需参数更新的头感知可控动作迁移框架：通过语义对应引导优化运动头输出，并选择性注入结构特征以保持画面完整性。该方法不仅实现了精准的动作迁移，还为可调控视频生成提供了可解释的基础。
原文摘要 · Abstract (English)
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.
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