We introduce an approach for augmenting text-to-video generation models with
customized motions, extending their capabilities beyond the motions depicted in
the original training data. By leveraging a few video samples demonstrating
specific movements as input, our method learns and generalizes the input motion
patterns for diverse, text-specified scenarios. Our contributions are
threefold. First, to achieve our results, we finetune an existing text-to-video
model to learn a novel mapping between the depicted motion in the input
examples to a new unique token. To avoid overfitting to the new custom motion,
we introduce an approach for regularization over videos. Second, by leveraging
the motion priors in a pretrained model, our method can produce novel videos
featuring multiple people doing the custom motion, and can invoke the motion in
combination with other motions. Furthermore, our approach extends to the
multimodal customization of motion and appearance of individualized subjects,
enabling the generation of videos featuring unique characters and distinct
motions. Third, to validate our method, we introduce an approach for
quantitatively evaluating the learned custom motion and perform a systematic
ablation study. We show that our method significantly outperforms prior
appearance-based customization approaches when extended to the motion
customization task.

通过扩展现有的文本到视频生成模型，我们引入了一种方法来增加定制动作，以超越原始训练数据中所描绘的动作。通过利用几个呈现特定动作的视频样本作为输入，我们的方法学习并概括了多样化的、与文本指定情景相关的输入动作模式。