  This paper presents a new practical training method for human matting, which
demands delicate pixel-level human region identification and significantly
laborious annotations. To reduce the annotation cost, most existing matting
approaches often rely on image synthesis to augment the dataset. However, the
unnaturalness of synthesized training images brings in a new domain
generalization challenge for natural images. To address this challenge, we
introduce a new learning paradigm, weakly semi-supervised human matting
(WSSHM), which leverages a small amount of expensive matte labels and a large
amount of budget-friendly segmentation labels, to save the annotation cost and
resolve the domain generalization problem. To achieve the goal of WSSHM, we
propose a simple and effective training method, named Matte Label Blending
(MLB), that selectively guides only the beneficial knowledge of the
segmentation and matte data to the matting model. Extensive experiments with
our detailed analysis demonstrate our method can substantially improve the
robustness of the matting model using a few matte data and numerous
segmentation data. Our training method is also easily applicable to real-time
models, achieving competitive accuracy with breakneck inference speed (328 FPS
on NVIDIA V100 GPU). The implementation code is available at
\url{https://github.com/clovaai/WSSHM}.
