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Customized generation using diffusion models has made impressive progress inimage generation, but remains unsatisfactory in the challenging videogeneration task, as it requires the controllability of both subjects andmotions. To that end, we present DreamVideo, a novel approach to generatingpersonalized videos from a few static images of the desired subject and a fewvideos of target motion. DreamVideo decouples this task into two stages,subject learning and motion learning, by leveraging a pre-trained videodiffusion model. The subject learning aims to accurately capture the fineappearance of the subject from provided images, which is achieved by combiningtextual inversion and fine-tuning of our carefully designed identity adapter.In motion learning, we architect a motion adapter and fine-tune it on the givenvideos to effectively model the target motion pattern. Combining these twolightweight and efficient adapters allows for flexible customization of anysubject with any motion. Extensive experimental results demonstrate thesuperior performance of our DreamVideo over the state-of-the-art methods forcustomized video generation. Our project page is athttps://dreamvideo-t2v.github.io.

DreamVideo: Composing Your Dream Videos with Customized Subject and Motion