One Shot Video Tuning

One-shot video tuning aims to adapt pre-trained image or video models to specific videos with minimal additional training, enabling efficient video editing and manipulation. Current research focuses on leveraging diffusion models, often incorporating techniques like ControlNet for precise object insertion and extended attention mechanisms for temporal coherence, or employing noise constraints to improve video smoothness. This approach offers a significant advancement by reducing the need for extensive video-specific datasets and computational resources, thereby accelerating progress in video generation and editing applications.