In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human dance videos. Specifically, given target identity and posture sequences, DreaMoving can generate a video of the target identity dancing anywhere driven by the posture sequences. To this end, we propose a Video ControlNet for motion-controlling and a Content Guider for identity preserving. The proposed model is easy to use and can be adapted to most stylized diffusion models to generate diverse results. The project page is available at https://dreamoving.github.io/dreamoving.

翻译：本文提出DreaMoving，一种基于扩散模型的可控视频生成框架，用于生成高质量定制化人体舞蹈视频。具体而言，给定目标身份与姿态序列，DreaMoving能够生成目标身份在任意场景中由姿态序列驱动的舞蹈视频。为此，我们提出用于运动控制的Video ControlNet和用于身份保持的Content Guider。该模型易于使用，可适配大多数风格化扩散模型以生成多样化的结果。项目页面见https://dreamoving.github.io/dreamoving。