MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement

• URL: http://arxiv.org/abs/2505.23742v2
• Date: Thu, 09 Oct 2025 08:41:12 GMT
• Title: MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
• Authors: Yufan Deng, Yuanyang Yin, Xun Guo, Yizhi Wang, Jacob Zhiyuan Fang, Shenghai Yuan, Yiding Yang, Angtian Wang, Bo Liu, Haibin Huang, Chongyang Ma,
• Abstract summary: We introduce MAGREF, a unified and effective framework for any-reference video generation.<n>Our approach incorporates masked guidance and a subject disentanglement mechanism.<n>Experiments on a comprehensive benchmark demonstrate that MAGREF consistently outperforms existing state-of-the-art approaches.
• Score: 47.064467920954776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle the task of any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, together with textual prompts. This task faces persistent challenges, including identity inconsistency, entanglement among multiple reference subjects, and copy-paste artifacts. To address these issues, we introduce MAGREF, a unified and effective framework for any-reference video generation. Our approach incorporates masked guidance and a subject disentanglement mechanism, enabling flexible synthesis conditioned on diverse reference images and textual prompts. Specifically, masked guidance employs a region-aware masking mechanism combined with pixel-wise channel concatenation to preserve appearance features of multiple subjects along the channel dimension. This design preserves identity consistency and maintains the capabilities of the pre-trained backbone, without requiring any architectural changes. To mitigate subject confusion, we introduce a subject disentanglement mechanism which injects the semantic values of each subject derived from the text condition into its corresponding visual region. Additionally, we establish a four-stage data pipeline to construct diverse training pairs, effectively alleviating copy-paste artifacts. Extensive experiments on a comprehensive benchmark demonstrate that MAGREF consistently outperforms existing state-of-the-art approaches, paving the way for scalable, controllable, and high-fidelity any-reference video synthesis. Code and model can be found at: https://github.com/MAGREF-Video/MAGREF

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