DreaMoving: A Human Dance Video Generation Framework based on Diffusion
  Models
Authors: name: Mengyang Feng, name: Jinlin Liu, name: Kai Yu, name: Yuan Yao, name: Zheng Hui, name: Xiefan Guo, name: Xianhui Lin, name: Haolan Xue, name: Chen Shi, name: Xiaowen Li, name: Aojie Li, name: Miaomiao Cui, name: Peiran Ren, name: Xuansong Xie

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.