Sangbeom Lim, Seoung Wug Oh, Heeji Yoon, Seungryong Kim, Joon-Young Lee · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26857 · open-access PDF
基于传播的视频对照最近出现了作为一个领先的方法,利用互联网规模的预训练以实现强大的零射线从有限的合成数据的普遍化。然而,这些传播背骨的沉重计算限制了推断到仅6到24个短片,所以即使是普通的视频也覆盖了多个片段。 因为每个片段都是独立于粗糙的面具指导而被模糊的,现有的方法无法在片段边界保持一致性,产生闪烁的边界和不间断的透明度。
Machine translation (Meta M2M100); the English original follows.
Diffusion-based video matting has recently emerged as a leading approach, leveraging video diffusion priors from internet-scale pretraining to achieve strong zero-shot generalization from limited synthetic data. However, the heavy compute of these diffusion backbones limits inference to a short chunk of just 6 to 24 frames, so even ordinary videos span multiple chunks. Because each chunk is matted independently from coarse mask guidance, existing methods fail to maintain consistency across chunk boundaries, producing flickering edges and discontinuous transparency. We address this with a mask-
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