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 di cult 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 Semantic Human Matting (SHM). SHM is the rst algorithm that learns to jointly t both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and ne 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.
Chen et al. REF proposed a Semantic Human Matting that integrated a semantic segmentation network with a matting network to automatically extract the alpha matte of humans.
Semantic Human Matting
MM '18
Computer Science Mathematics