MotionV2V：在视频中编辑运动
MotionV2V: Editing Motion in a Video
把「运动编辑」显式参数化为输入/输出稀疏轨迹之间的偏差，并用构造的运动反事实视频对微调运动条件扩散模型，为视频编辑提供了一条可复用路径：用轨迹级控制信号替代文本提示来精确操控运动，且编辑可从任意时间戳起始并自然传播。
原文依据 we propose modifying video motion by directly editing sparse trajectories extracted from the input
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管生成式视频模型在保真度与一致性上已取得显著进展，但将这些能力用于视频编辑仍是一项复杂的挑战。近期研究已广泛探索运动可控性，以增强文本到视频生成或图像动画；然而，我们发现精确的运动控制是一种有前景但尚未被充分探索的既有视频编辑范式。在本工作中，我们提出通过直接编辑从输入中提取的稀疏轨迹来修改视频运动。我们将输入轨迹与输出轨迹之间的偏差称为「运动编辑」，并证明该表示在与生成式骨干网络结合后，能够实现多种强大的视频编辑能力。为实现这一点，我们引入了一条新颖的流程来生成「运动反事实」——即内容相同但运动不同的视频对——并在该数据集上微调一个以运动为条件的视频扩散架构。我们的方法支持从任意时间戳开始并自然传播的编辑。在一项四选一的头对头用户研究中，我们的模型相对于先前工作获得了超过 65% 的偏好率。
英文原文摘要 1139 字符
While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has extensively explored motion controllability as a means to enhance text-to-video generation or image animation; however, we identify precise motion control as a promising, yet under-explored, paradigm for editing existing videos. In this work, we propose modifying video motion by directly editing sparse trajectories extracted from the input. We term the deviation between input and output trajectories a 'motion edit' and demonstrate that this representation, when coupled with a generative backbone, enables many powerful video editing capabilities. To achieve this, we introduce a novel pipeline for generating `motion counterfactuals' -- video pairs that share identical content but distinct motion -- and fine-tune a motion-conditioned video diffusion architecture on this dataset. Our approach allows for edits that start at any timestamp and propagate naturally. In a 4-way head-to-head user study, our model achieves over 65% preference against prior work.