  We in this paper solve the problem of high-quality automatic real-time
background cut for 720p portrait videos. We first handle the background
ambiguity issue in semantic segmentation by proposing a global background
attenuation model. A spatial-temporal refinement network is developed to
further refine the segmentation errors in each frame and ensure temporal
coherence in the segmentation map. We form an end-to-end network for training
and testing. Each module is designed considering efficiency and accuracy. We
build a portrait dataset, which includes 8,000 images with high-quality labeled
map for training and testing. To further improve the performance, we build a
portrait video dataset with 50 sequences to fine-tune video segmentation. Our
framework benefits many video processing applications.
