MatAnyone 2 is a human video matting framework that leverages AI—notably a learned Matting Quality Evaluator (MQE) and memory-based reference-frame training—to produce high-quality alpha mattes with fine boundary detail and strong semantic stability; the MQE gives pixel-wise, no-ground-truth quality scores used as online training feedback and offline data curation to scale learning on the large VMReal dataset. It’s ideal for video editors, VFX/AR developers, and researchers who need robust, production-ready foreground extraction from challenging real-world footage, offering better boundary fidelity and temporal consistency than prior methods.