  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 https://github.com/yucornetto/MGMatting.
