x-RAGE: eXtended Reality -- Action & Gesture Events Dataset

TL;DR

This paper introduces x-RAGE, the first egocentric gesture dataset captured with event-based cameras, aimed at advancing low-power, neuromorphic gesture recognition for XR devices in the Metaverse.

Contribution

It provides a novel event-camera based dataset for egocentric gestures, addressing limitations of traditional frame-based methods in XR applications.

Findings

First egocentric event-camera gesture dataset for XR

Enables low-power neuromorphic gesture recognition

Supports development of real-time, fast-motion gesture recognition

Abstract

With the emergence of the Metaverse and focus on wearable devices in the recent years gesture based human-computer interaction has gained significance. To enable gesture recognition for VR/AR headsets and glasses several datasets focusing on egocentric i.e. first-person view have emerged in recent years. However, standard frame-based vision suffers from limitations in data bandwidth requirements as well as ability to capture fast motions. To overcome these limitation bio-inspired approaches such as event-based cameras present an attractive alternative. In this work, we present the first event-camera based egocentric gesture dataset for enabling neuromorphic, low-power solutions for XR-centric gesture recognition. The dataset has been made available publicly at the following URL: https://gitlab.com/NVM_IITD_Research/xrage.