We propose Mask Guided (MG) Matting, a robust matting framework that takes a
general coarse mask as guidance. MG Matting leverages a network (PRN) design
which encourages the matting model to provide self-guidance to progressively
refine the uncertain regions through the decoding process. A series of guidance
mask perturbation operations are also introduced in the training to further
enhance its robustness to external guidance. We show that PRN can generalize to
unseen types of guidance masks such as trimap and low-quality alpha matte,
making it suitable for various application pipelines. In addition, we revisit
the foreground color prediction problem for matting and propose a surprisingly
simple improvement to address the dataset issue. Evaluation on real and
synthetic benchmarks shows that MG Matting achieves state-of-the-art
performance using various types of guidance inputs. Code and models are
available at this https URL

我们提出了一种名为 Mask Guided Matting 的稳健抠图框架，它以一般粗略掩膜为指导，利用 PRN 网络设计逐步优化不确定区域，同时通过一系列指导掩膜扰动操作增强其对外部指导的稳健性。我们重新审视了前景色预测问题，提出了一个简单的改进方案以解决数据集问题。在真实和合成基准测试中，我们证明了 MG Matting 利用各种类型的指导输入实现了最先进的性能。