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.

翻译：视频扩散模型已被开发用于视频生成，通常结合文本和图像条件约束以增强对生成内容的控制。尽管取得进展，但确保帧间一致性仍是挑战，尤其在以文本提示作为控制条件时。针对此问题，我们提出UniCtrl——一种新型即插即用方法，无需额外训练即可普适性地提升文本到视频模型所生成视频的时空一致性与运动多样性。UniCtrl通过跨帧自注意力控制确保不同帧间的语义一致性，同时通过运动注入与时空同步增强运动质量与时空一致性。实验结果表明，UniCtrl在增强多种文本到视频模型方面的效能，验证了其有效性与普适性。