Saber is a zero-shot framework for reference-to-video generation that relies solely on video-text pairs instead of costly reference image-video-text triplets. It uses masked training with dynamic substitutes to enhance subject integration and generalization across diverse scenarios. The model shows improved performance in generating videos that maintain subject identity while following text prompts.