DreaMoving: A Human Video Generation Framework based on Diffusion Models
This work addresses the need for customizable human video generation in applications like entertainment or virtual avatars, representing an incremental advancement in diffusion model applications.
The paper tackles the problem of generating high-quality customized human videos from target identity and posture sequences using a diffusion-based framework, achieving controllable video generation with identity preservation and motion control.
In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos. Specifically, given target identity and posture sequences, DreaMoving can generate a video of the target identity moving or 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