# Egocentric Human Segmentation for Mixed Reality

arXiv: https://arxiv.org/abs/2005.12074 · HTML: https://mlsanity.com/paper/2005.12074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 2 of 2)

- [Real Time Egocentric Object Segmentation: THU-READ Labeling and Benchmarking Results](https://mlsanity.com/paper/2106.04957/) (2021)
- [Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario](https://mlsanity.com/paper/2106.06403/) (2021)

## Abstract (arXiv)

The objective of this work is to segment human body parts from egocentric video using semantic segmentation networks. Our contribution is two-fold: i) we create a semi-synthetic dataset composed of more than 15, 000 realistic images and associated pixel-wise labels of egocentric human body parts, such as arms or legs including different demographic factors; ii) building upon the ThunderNet architecture, we implement a deep learning semantic segmentation algorithm that is able to perform beyond real-time requirements (16 ms for 720 x 720 images). It is believed that this method will enhance sense of presence of Virtual Environments and will constitute a more realistic solution to the standard virtual avatars.