MAGREF: Masked Guidance for Any-Reference Video Generation

• URL: http://arxiv.org/abs/2505.23742v1
• Date: Thu, 29 May 2025 17:58:15 GMT
• Title: MAGREF: Masked Guidance for Any-Reference Video Generation
• Authors: Yufan Deng, Xun Guo, Yuanyang Yin, Jacob Zhiyuan Fang, Yiding Yang, Yizhi Wang, Shenghai Yuan, Angtian Wang, Bo Liu, Haibin Huang, Chongyang Ma,
• Abstract summary: MAGREF is a unified framework for any-reference video generation.<n>We propose a region-aware dynamic masking mechanism that enables a single model to flexibly handle various subject inference.<n>Our model delivers state-of-the-art video generation quality, generalizing from single-subject training to complex multi-subject scenarios.
• Score: 33.35245169242822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video generation has made substantial strides with the emergence of deep generative models, especially diffusion-based approaches. However, video generation based on multiple reference subjects still faces significant challenges in maintaining multi-subject consistency and ensuring high generation quality. In this paper, we propose MAGREF, a unified framework for any-reference video generation that introduces masked guidance to enable coherent multi-subject video synthesis conditioned on diverse reference images and a textual prompt. Specifically, we propose (1) a region-aware dynamic masking mechanism that enables a single model to flexibly handle various subject inference, including humans, objects, and backgrounds, without architectural changes, and (2) a pixel-wise channel concatenation mechanism that operates on the channel dimension to better preserve appearance features. Our model delivers state-of-the-art video generation quality, generalizing from single-subject training to complex multi-subject scenarios with coherent synthesis and precise control over individual subjects, outperforming existing open-source and commercial baselines. To facilitate evaluation, we also introduce a comprehensive multi-subject video benchmark. Extensive experiments demonstrate the effectiveness of our approach, paving the way for scalable, controllable, and high-fidelity multi-subject video synthesis. Code and model can be found at: https://github.com/MAGREF-Video/MAGREF

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