Automatic Real-time Background Cut for Portrait Videos
This addresses the need for efficient background segmentation in portrait videos for video processing applications, representing an incremental advancement.
The paper tackles the problem of high-quality automatic real-time background removal for 720p portrait videos by proposing an end-to-end network with a global background attenuation model and spatial-temporal refinement, achieving unspecified performance improvements.
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.