# Flow-based Video Segmentation for Human Head and Shoulders

arXiv: https://arxiv.org/abs/2104.09752 · HTML: https://mlsanity.com/paper/2104.09752/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network](https://mlsanity.com/paper/1707.07584/) (2017)

## Built on by (most cited first, 4 of 4)

- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [PP-HumanSeg: Connectivity-Aware Portrait Segmentation with a Large-Scale Teleconferencing Video Dataset](https://mlsanity.com/paper/2112.07146/) (2021)
- [Application Of ADNN For Background Subtraction In Smart Surveillance System](https://mlsanity.com/paper/2301.00264/) (2022)
- [EasyPortrait -- Face Parsing and Portrait Segmentation Dataset](https://mlsanity.com/paper/2304.13509/) (2023)

## Abstract (arXiv)

Video segmentation for the human head and shoulders is essential in creating elegant media for videoconferencing and virtual reality applications. The main challenge is to process high-quality background subtraction in a real-time manner and address the segmentation issues under motion blurs, e.g., shaking the head or waving hands during conference video. To overcome the motion blur problem in video segmentation, we propose a novel flow-based encoder-decoder network (FUNet) that combines both traditional Horn-Schunck optical-flow estimation technique and convolutional neural networks to perform robust real-time video segmentation. We also introduce a video and image segmentation dataset: ConferenceVideoSegmentationDataset. Code and pre-trained models are available on our GitHub repository: urlhttps://github.com/kuangzijian/Flow-Based-Video-Matting.