# xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera

## TL;DR

This paper introduces a novel egocentric 3D human pose estimation method using a specialized neural network architecture and a large synthetic dataset, achieving state-of-the-art results in challenging VR viewpoints.

## Contribution

The paper presents a new encoder-decoder architecture with a dual branch decoder for egocentric pose estimation and introduces the large-scale synthetic dataset xR-EgoPose for training and evaluation.

## Key findings

- Significant accuracy improvements over existing methods.
- Good generalization from synthetic to real-world data.
- Competitive performance on Human3.6M benchmark.

## Abstract

We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint, just 2 cm. away from the user's face, leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. Our contribution is two-fold. Firstly, we propose a new encoder-decoder architecture with a novel dual branch decoder designed specifically to account for the varying uncertainty in the 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. Our second contribution is a new large-scale photorealistic synthetic dataset - xR-EgoPose - offering 383K frames of high quality renderings of people with a diversity of skin tones, body shapes, clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.