Human matting, high quality extraction of humans from natural images, is
crucial for a wide variety of applications. Since the matting problem is
severely under-constrained, most previous methods require user interactions to
take user designated trimaps or scribbles as constraints. This user-in-the-loop
nature makes them difficult to be applied to large scale data or time-sensitive
scenarios. In this paper, instead of using explicit user input constraints, we
employ implicit semantic constraints learned from data and propose an automatic
human matting algorithm (SHM). SHM is the first algorithm that learns to
jointly fit both semantic information and high quality details with deep
networks. In practice, simultaneously learning both coarse semantics and fine
details is challenging. We propose a novel fusion strategy which naturally
gives a probabilistic estimation of the alpha matte. We also construct a very
large dataset with high quality annotations consisting of 35,513 unique
foregrounds to facilitate the learning and evaluation of human matting.
Extensive experiments on this dataset and plenty of real images show that SHM
achieves comparable results with state-of-the-art interactive matting methods.