点到点：用于可控视频编辑的稀疏运动引导
Point-to-Point: Sparse Motion Guidance for Controllable Video Editing
AI总结：
Point-to-Point通过引入锚点标记，利用视频扩散模型的先验知识，实现可控视频编辑中运动与编辑的高质量保真度。
摘要：
在视频编辑任务中，准确保留运动仍是一个核心挑战。现有方法常常在编辑和运动保真度之间面临权衡，因为它们依赖于运动表示，这些表示要么过度拟合布局，要么仅隐式定义。为克服这一限制，我们重新审视基于点的运动表示。然而，在没有人工输入的情况下，识别有意义的点仍然具有挑战性，尤其是在多样的视频场景中。为此，我们提出了一种新的运动表示方法，即锚点标记，通过利用视频扩散模型丰富的先验知识，捕捉最本质的运动模式。锚点标记通过少量的信息性点轨迹紧凑地编码视频动态，并且可以灵活地重新定位以与新主体对齐。这使我们的方法Point-to-Point能够在多样化的场景中进行泛化。广泛的实验表明，锚点标记导致了更加可控和语义对齐的视频编辑，实现了编辑和运动保真度方面的优越性能。
英文摘要：
Accurately preserving motion while editing a subject remains a core challenge in video editing tasks. Existing methods often face a trade-off between edit and motion fidelity, as they rely on motion representations that are either overfitted to the layout or only implicitly defined. To overcome this limitation, we revisit point-based motion representation. However, identifying meaningful points remains challenging without human input, especially across diverse video scenarios. To address this, we propose a novel motion representation, anchor tokens, that capture the most essential motion patterns by leveraging the rich prior of a video diffusion model. Anchor tokens encode video dynamics compactly through a small number of informative point trajectories and can be flexibly relocated to align with new subjects. This allows our method, Point-to-Point, to generalize across diverse scenarios. Extensive experiments demonstrate that anchor tokens lead to more controllable and semantically aligned video edits, achieving superior performance in terms of edit and motion fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
