# Absolute Human Pose Estimation with Depth Prediction Network

## TL;DR

This paper introduces a neural network that directly predicts absolute 3D human joint positions in camera coordinates, simplifying the process and improving accuracy for multi-person scenarios.

## Contribution

It proposes a novel single-step neural network approach for absolute human pose estimation without post-processing, outperforming previous methods on MuPoTS-3D.

## Key findings

- Achieves state-of-the-art results on MuPoTS-3D dataset
- Outperforms previous methods in multi-person pose estimation
- Works in a single step without post-processing

## Abstract

The common approach to 3D human pose estimation is predicting the body joint coordinates relative to the hip. This works well for a single person but is insufficient in the case of multiple interacting people. Methods predicting absolute coordinates first estimate a root-relative pose then calculate the translation via a secondary optimization task. We propose a neural network that predicts joints in a camera centered coordinate system instead of a root-relative one. Unlike previous methods, our network works in a single step without any post-processing. Our network beats previous methods on the MuPoTS-3D dataset and achieves state-of-the-art results.