SAM2Matting: Generalized Image and Video Matting with a Tracker-to-Matting Framework

Forum topic · 小凯 · 2026-06-27

Summary

SAM2Matting is a new framework for generalized image and video matting presented by Ruiqi Shen, Guangquan Jie, and Chang Liu in arXiv paper 2606.27339. The authors observe that despite impressive advances in image matting, video matting remains challenging because of an inherent gap between high-level tracking, which requires frame-wise understanding, and low-level matting, which demands extremely fine-grained detail. Existing approaches rely on expensive and narrowly-scoped video matting datasets, which can limit out-of-domain generalization and weaken tracking robustness. SAM2Matting rethinks this paradigm as a tracker-to-matting framework: it leverages the powerful tracking capabilities of SAM2 to produce high-quality matting results without requiring costly video matting datasets. Reported on zhichai.net as part of its arXiv paper aggregation, this work offers a data-efficient path toward robust video matting with improved generalization across domains.

Paper Overview

Research Area: Computer Vision (CV) Authors: Ruiqi Shen, Guangquan Jie, Chang Liu Published: 2026-06-27 arXiv: 2606.27339

Abstract

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.

The authors rethink the paradigm with SAM2Matting, a tracker-to-matting framework that leverages SAM2's tracking capabilities to generate high-quality matting results without requiring expensive video matting datasets.

Key Points

Video matting is harder than image matting because it combines two tasks with very different requirements: frame-level tracking and pixel-level fine detail extraction.
Prior methods depend on costly, narrow video matting datasets, hurting out-of-domain generalization and tracking robustness.
SAM2Matting converts SAM2's tracking capability into matting quality, bridging the tracker-to-matting gap.
The framework avoids the need for expensive video matting data collection.

--- *Automatically collected on 2026-06-27*