Videos convey richer information than images or text, capturing both spatial and temporal dynamics. However, most existing video customization methods rely on reference images or task-specific temporal priors, failing to fully exploit the rich spatio-temporal information inherent in videos, thereby limiting flexibility and generalization in video generation. To address these limitations, we propose OmniTransfer, a unified framework for spatio-temporal video transfer. It leverages multi-view information across frames to enhance appearance consistency and exploits temporal cues to enable fine-grained temporal control. To unify various video transfer tasks, OmniTransfer incorporates three key designs: Task-aware Positional Bias that adaptively leverages reference video information to improve temporal alignment or appearance consistency; Reference-decoupled Causal Learning separating reference and target branches to enable precise reference transfer while improving efficiency; and Task-adaptive Multimodal Alignment using multimodal semantic guidance to dynamically distinguish and tackle different tasks. Extensive experiments show that OmniTransfer outperforms existing methods in appearance (ID and style) and temporal transfer (camera movement and video effects), while matching pose-guided methods in motion transfer without using pose, establishing a new paradigm for flexible, high-fidelity video generation.

翻译：视频比图像或文本传递更丰富的信息，同时捕捉空间和时间动态。然而，现有的大多数视频定制方法依赖于参考图像或任务特定的时间先验，未能充分利用视频中固有的丰富时空信息，从而限制了视频生成的灵活性和泛化能力。为解决这些局限性，我们提出了OmniTransfer，一个用于时空视频迁移的统一框架。它利用跨帧的多视角信息来增强外观一致性，并利用时间线索实现细粒度的时间控制。为统一各种视频迁移任务，OmniTransfer融合了三个关键设计：任务感知位置偏置，自适应地利用参考视频信息以改善时间对齐或外观一致性；参考解耦因果学习，将参考分支与目标分支分离，以实现精确的参考迁移并提升效率；以及任务自适应多模态对齐，利用多模态语义引导动态区分和处理不同任务。大量实验表明，OmniTransfer在外观（身份与风格）和时间迁移（摄像机运动与视频特效）方面优于现有方法，同时在无需使用姿态的情况下，在运动迁移任务中达到与姿态引导方法相当的性能，为灵活、高保真的视频生成建立了新范式。