Abstract
Comment: ECCV 2026. Extended version. Project Page: https://henghuiding.com/SAM2Matting/
SAM2Matting: Generalized Image and Video Matting
- Authors
- Ruiqi Shen, Guangquan Jie, Chang Liu...
- Categories
- cs.CV
Brief
SAM2Matting reframes video matting as a tracker-to-matting pipeline: a high-fidelity matting module plus region-proposal bridge built on SAM-family trackers preserves temporal robustness while matting heads recover fine detail. Despite image-only training, authors report SOTA video-matting performance, diverse prompt support, and strong cross-domain generalization. Only the paper's abstract was available; full-text evaluation details are not provided.
Why it matters
SAM2Matting (Ruiqi Shen, Guangquan Jie, Chang Liu, Henghui Ding; arXiv 2026-06-25; ECCV 2026 extended) is a tracker-to-matting framework that augments foundational VOS trackers (e.g., SAM2, SAM3) with a region-proposal bridge and dedicated matting heads, decoupling temporal tracking from fine-grained matting.
Key details
- Despite being trained only on images, SAM2Matting claims new state-of-the-art video-matting performance, supports diverse prompt types, maintains strong temporal consistency, and generalizes across human-centric and in-the-wild scenarios.