SAM2Matting: Generalized Image and Video Matting

Ruiqi Shen, Guangquan Jie, Chang Liu, Henghui Ding

SAM2Matting: Generalized Image and Video Matting: 9 upvotes on Hugging Face Daily Papers, #23 of 49 papers on 2026-06-30. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Despite impressive advances in image matting, video matting remains challenging due to the inherent gap between high-level tracking, which requires frame-wise understanding, and low-level matting, which focuses on extremely fine-grained details. Existing methods attempt this with expensive and narrowly-scoped video matting datasets, which may limit out-of-domain generalization and compromise tracking robustness. We rethink the paradigm with SAM2Matting, a tracker-to-matting framework that advances VOS trackers to high-fidelity video matting. Specifically, it decouples the task by enhancing a foundational tracker (e.g., SAM2, SAM3) with a region-proposal bridge and dedicated matting heads, enabling the uncompromised tracker to handle temporal consistency while the matting components resolve fine-grained details. Notably, despite being trained only on images, SAM2Matting establishes new state-of-the-art performance on video matting, supports diverse prompt types, maintains strong temporal consistency, and demonstrates robust generalization across both human-centric and in-the-wild scenarios.

Paper page on Hugging Face · arXiv

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