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.
Given a small set of exemplar videos, our approach fine-tunes the U-Net of a text-to-video model using a reconstruction objective. The motion is identified with a unique motion identifier and can be used at test time to synthesize novel subjects performing the motion. To represent the added motion but preserve information from the pretrained model, we tune a subset of weights -- the temporal convolution and attention layers, in addition to the key and value layers in the spatial attention layer. A set of related videos is used to regularize the tuning process.