Mask Guided Matting via Progressive Refinement Network
This work provides a more robust and generalizable matting solution for various applications by handling diverse and coarse guidance masks.
This paper introduces Mask Guided (MG) Matting, a framework that refines uncertain regions using a Progressive Refinement Network (PRN) and self-guidance. It achieves state-of-the-art performance on real and synthetic benchmarks using various guidance inputs.
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.