💡 The paper introduces DomainShuttle, a method for open domain subject-driven text-to-video generation that achieves high fidelity and flexibility across different scenarios. The problem with existing methods is that they focus on maximizing subject fidelity in in-domain scenarios, which limits their ability to adapt to cross-domain scenarios where subject-irrelevant properties need to vary according to the text prompt. DomainShuttle addresses this issue by introducing domain-aware modeling and a dual RoPE scheme. The method uses Domain-MoT to decouple videos and reference features, and domain-aware AdaLN for domain-specific modeling of reference images. It also uses the Video-Reference DualRoPE scheme to enable precise subject-level spatial modeling, and Cross-Pair Consistent Loss to extract intrinsic subject features unaffected by irrelevant features. The results of extensive experiments show that DomainShuttle achieves significant performance improvements over existing methods, demonstrating high subject fidelity and generative flexibility across diverse open domain application scenarios. This means that DomainShuttle can generate high-quality videos that retain the key features of the subject while also allowing for flexible editing and adaptation to different styles, semantic combinations, or domain attributes. Overall, the paper proposes a novel approach to text-to-video generation that can flexibly shuttle between different domains, making it a valuable contribution to the field of open domain subject-driven text-to-video generation.