x-RAGE: eXtended Reality -- Action & Gesture Events Dataset
This addresses the need for efficient gesture interaction in XR devices, though it is incremental as it builds on existing datasets by introducing a new sensor modality.
The authors tackled the problem of gesture recognition for VR/AR by creating the first event-camera based egocentric gesture dataset, which is publicly available to enable neuromorphic, low-power solutions.
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.