# Metric Pose Estimation for Human-Machine Interaction Using Monocular Vision

arXiv: https://arxiv.org/abs/1910.03239 · HTML: https://mlsanity.com/paper/1910.03239/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

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

- [ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics](https://mlsanity.com/paper/2106.01981/) (2021)
- [Human from Blur: Human Pose Tracking from Blurry Images](https://mlsanity.com/paper/2303.17209/) (2023)

## Abstract (arXiv)

The rapid growth of collaborative robotics in production requires new automation technologies that take human and machine equally into account. In this work, we describe a monocular camera based system to detect human-machine interactions from a bird's-eye perspective. Our system predicts poses of humans and robots from a single wide-angle color image. Even though our approach works on 2D color input, we lift the majority of detections to a metric 3D space. Our system merges pose information with predefined virtual sensors to coordinate human-machine interactions. We demonstrate the advantages of our system in three use cases.