OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer
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.
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Pengze Zhang
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2026-01-21T09:19:32.663Z
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PangzeCheung
PangzeCheung
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PangzeCheung
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Yanze Wu
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Mengtian Li
admin_assigned
2026-01-21T10:52:30.670Z
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Mengtian Li
LemonSky1995
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LemonSky1995
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Xu Bai
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Songtao Zhao
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Fulong Ye
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2026-01-21T11:46:59.343Z
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fulong ye
Alon77777
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Alon77777
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Chong Mou
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Xinghui Li
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2026-01-21T10:52:58.973Z
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Xinghui Li
Crayon-Shinchan
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Crayon-Shinchan
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Zhuowei Chen
admin_assigned
2026-01-21T10:52:20.177Z
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Zhuowei_Chen
ZhuoweiChen
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ZhuoweiChen
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Qian He
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Mingyuan Gao
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2026-01-21T10:52:13.464Z
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Mingyuan Gao
GMY1999
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GMY1999
OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer
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.
OmniTransfer presents a unified framework for spatio-temporal video transfer that enhances appearance consistency and temporal control through multi-view information and multimodal semantic guidance.
video customization spatio-temporal video transfer multi-view information temporal cues temporal alignment appearance consistency reference-decoupled causal learning task-adaptive multimodal alignment pose-guided methods