# Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality

arXiv: https://arxiv.org/abs/2001.00487 · HTML: https://mlsanity.com/paper/2001.00487/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

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

- [Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality](https://mlsanity.com/paper/2207.01296/) (2022)
- [Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks](https://mlsanity.com/paper/2102.01460/) (2021)
- [Full Body Video-Based Self-Avatars for Mixed Reality: from E2E System to User Study](https://mlsanity.com/paper/2208.12639/) (2022)
- [Applying Deep Learning for cockpit segmentation in the context of mixed reality](https://mlsanity.com/paper/2606.06520/) (2026)

## Abstract (arXiv)

In this paper, we present preliminary results on the use of deep learning techniques to integrate the users self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing users bodies in such simulations may improve the feeling of both self and social presence in the virtual environment, as well as user performance. We propose to use a convolutional neural network for real time semantic segmentation of users bodies in the stereoscopic RGB video streams acquired from the perspective of the user. We describe design issues as well as implementation details of the system and demonstrate the feasibility of using such neural networks for merging users bodies in an augmented virtuality simulation.