# Automatic Real-time Background Cut for Portrait Videos

arXiv: https://arxiv.org/abs/1704.08812 · HTML: https://mlsanity.com/paper/1704.08812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [Temporally Coherent Person Matting Trained on Fake-Motion Dataset](https://mlsanity.com/paper/2109.04843/) (2021)

## Abstract (arXiv)

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.