Image customization has been extensively studied in text-to-image (T2I)
diffusion models, leading to impressive outcomes and applications. With the
emergence of text-to-video (T2V) diffusion models, its temporal counterpart,
motion customization, has not yet been well investigated. To address the
challenge of one-shot motion customization, we propose Customize-A-Video that
models the motion from a single reference video and adapting it to new subjects
and scenes with both spatial and temporal varieties. It leverages low-rank
adaptation (LoRA) on temporal attention layers to tailor the pre-trained T2V
diffusion model for specific motion modeling from the reference videos. To
disentangle the spatial and temporal information during the training pipeline,
we introduce a novel concept of appearance absorbers that detach the original
appearance from the single reference video prior to motion learning. Our
proposed method can be easily extended to various downstream tasks, including
custom video generation and editing, video appearance customization, and
multiple motion combination, in a plug-and-play fashion. Our project page can
be found at this https URL

通过低秩适应的时空注意力层，该文献介绍了一种用于一次性运动定制的自定义视频模型，可以从单个参考视频中学习运动信息，以适应新的主题和场景，并可轻松扩展到多个下游任务。