  Video Diffusion Models have been developed for video generation, usually
integrating text and image conditioning to enhance control over the generated
content. Despite the progress, ensuring consistency across frames remains a
challenge, particularly when using text prompts as control conditions. To
address this problem, we introduce UniCtrl, a novel, plug-and-play method that
is universally applicable to improve the spatiotemporal consistency and motion
diversity of videos generated by text-to-video models without additional
training. UniCtrl ensures semantic consistency across different frames through
cross-frame self-attention control, and meanwhile, enhances the motion quality
and spatiotemporal consistency through motion injection and spatiotemporal
synchronization. Our experimental results demonstrate UniCtrl's efficacy in
enhancing various text-to-video models, confirming its effectiveness and
universality.
