Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a motion-centric, gradient-based data attribution framework that scales to modern, large, high-quality video datasets and models. We use this to study which fine-tuning clips improve or degrade temporal dynamics. Motive isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. On text-to-video models, Motive identifies clips that strongly affect motion and guides data curation that improves temporal consistency and physical plausibility. With Motive-selected high-influence data, our method improves both motion smoothness and dynamic degree on VBench, achieving a 74.1% human preference win rate compared with the pretrained base model. To our knowledge, this is the first framework to attribute motion rather than visual appearance in video generative models and to use it to curate fine-tuning data.

翻译：尽管视频生成模型发展迅速，但数据在影响运动方面的作用仍未得到充分理解。我们提出了Motive（视频生成中的运动归因），这是一个以运动为中心、基于梯度的数据归因框架，能够扩展到现代大规模高质量视频数据集和模型。我们利用该框架研究哪些微调片段会改善或损害时序动态。Motive通过运动加权损失掩码将时序动态与静态外观分离，实现了高效且可扩展的特定运动影响计算。在文本到视频模型中，Motive能够识别对运动有显著影响的视频片段，并指导数据筛选，从而提升时序一致性和物理合理性。使用Motive筛选出的高影响力数据，我们的方法在VBench基准上同时提升了运动平滑度和动态程度，与预训练基础模型相比获得了74.1%的人类偏好胜率。据我们所知，这是首个在视频生成模型中针对运动而非视觉外观进行归因，并以此指导微调数据筛选的框架。